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On-line measurement of grain quality with NIR technology
Abstract
During the summer season of 2000, a feasibility study is carried out to measure grain quality
(moisture and protein content) with Near Infrared Reflection (NIR) technology on a conventional
TX64 New Holland combine harvester. A Zeiss Corona 45 NIR 1.7 sensor was installed on a
bypass of the clean grain elevator. Parallel with the NIR measurement, grain samples were taken at
the end of the bubble-up auger that transports the grain from the clean grain elevator to the grain
bin.
Measured signals at the diode array of the spectrophotometer are highly variable in time and since
data is acquired at a sampling rate of 23 Hz, an appropriate low pass filter is designed to obtain
every second a relevant spectrum. In addition, an optimal time shift is calculated between the
location of the NIR measurement and the sampling spot, in order to make the comparison between
the estimated spectra and grain samples as accurate as possible.
The spectra are first spectrally converted to either absorbance or Kubelka-Munk values. The
required spectra of a white and black standard are time-interpolated between the standard
measurements executed closest before and after the selected on-line spectrum. Preprocessing,
calibration and validation are executed using the PLSplus/IQ module included in the GRAMS/32
software package. After application of Mean Centering, the Multiple Scatter Correction, Standard
Normal Variate and detrending algorithm are applied. The calibration models are developed using
the PLS algorithm and validated through cross-validation.
... NIR spectroscopy technique is one of the fast and nondestructive tools that could be used to detect several grain parameters earlier in the supply chain (Caporaso et al., 2018b;Huang et al., 2008;Singh et al., 2006). Researchers evidenced the capability of NIR spectroscopy in measuring parameters such as moisture (Dowell et al., 2006;Ibrahim et al., 2018;Maertens et al., 2004;Shi & Yu, 2017), crude protein (Delwiche & Hruschka, 2000;Lin et al., 2014;Long et al., 2008;Maertens et al., 2004;Shi & Yu, 2017;Williams, 2020), hardness , starch (Cozzolino et al., 2014;Ibrahim et al., 2018), protein quality (Baslar & Ertugay, 2011;Dowell et al., 2006;Ibrahim et al., 2018;Lü et al., 2017;Williams, 2020), toxins (Delwiche, 2003;Delwiche & Hareland, 2004;Levasseur-Garcia, 2018;Peiris et al., 2009), insect infestation (Banga et al., 2018;Maghirang et al., 2013;Johnson, 2020;Paliwal et al., 2004), and mold (Delwiche, 2003) for wheat. Scholars have also reported on the potential of NIR spectroscopy on FN (Delwiche et al., 2018;FACT: Predicting Wheat Hagberg Falling Number from Near Infrared Spectrometers, 2022;Hu et al., 2022;Risius et al., 2015), or ash content (Cereals and Grains Association, 2022; Sudar et al., 2007) determination. ...
... NIR spectroscopy technique is one of the fast and nondestructive tools that could be used to detect several grain parameters earlier in the supply chain (Caporaso et al., 2018b;Huang et al., 2008;Singh et al., 2006). Researchers evidenced the capability of NIR spectroscopy in measuring parameters such as moisture (Dowell et al., 2006;Ibrahim et al., 2018;Maertens et al., 2004;Shi & Yu, 2017), crude protein (Delwiche & Hruschka, 2000;Lin et al., 2014;Long et al., 2008;Maertens et al., 2004;Shi & Yu, 2017;Williams, 2020), hardness , starch (Cozzolino et al., 2014;Ibrahim et al., 2018), protein quality (Baslar & Ertugay, 2011;Dowell et al., 2006;Ibrahim et al., 2018;Lü et al., 2017;Williams, 2020), toxins (Delwiche, 2003;Delwiche & Hareland, 2004;Levasseur-Garcia, 2018;Peiris et al., 2009), insect infestation (Banga et al., 2018;Maghirang et al., 2013;Johnson, 2020;Paliwal et al., 2004), and mold (Delwiche, 2003) for wheat. Scholars have also reported on the potential of NIR spectroscopy on FN (Delwiche et al., 2018;FACT: Predicting Wheat Hagberg Falling Number from Near Infrared Spectrometers, 2022;Hu et al., 2022;Risius et al., 2015), or ash content (Cereals and Grains Association, 2022; Sudar et al., 2007) determination. ...
... This paper provides an overview of the potential effects of unit operations on wheat-based end-product qualities. This review has focused on hard red wheat due to its large international market and the depth of research Long et al., 2008;Maertens et al., 2004;Maghirang et al., , 2013Paliwal et al., 2004;Peiris et al., 2009;Risius et al., 2015;Shi & Yu, 2017;Singh et al., 2006;Williams, 2020) (Continues) Jayas, 2000aJayas, , 2000bJayas, , 2000cJayas, , 2000dPaliwal et al., 2003;Qiu et al., 2019;Ridgway et al., 2002;Sabanci et al., 2017;Vithu & Moses, 2016) NIR Hyperspectral * * * * * * * * * * * (Barbedo et al., 2018;Caporaso et al., 2018a;Delwiche et al., 2019;Erkinbaev et al., 2019Erkinbaev et al., , 2022Femenias et al., 2020aFemenias et al., , 2020bFemenias, Bainotti, et al., 2021;Fox & Manley, 2014;Mahesh et al., 2015;Serranti et al., 2013;Shen et al., 2022;Singh et al., 2009;Zhang et al., Guo et al., 2013;M. Jafari et al., 2020;Jha et al., 2011;Lawrence et al., 2001;Rai et al., 2010) Microwave/ radiowave imaging * * * * (Asefi et al., 2015(Asefi et al., , 2017Gilmore et al., 2017;Lovetri et al., 2020;Reimer et al., 2018) Raman * * (Egging et al., 2018;Jia et al., 2020;Kłosok et al., 2021;Moskovskiy et al., 2021;Stawoska et al., 2021) NMR * * * * * (Salimi Khorshidi et al., 2018;Schinabeck et al., 2018;Shank et al., 2011;Shao et al., 2018;Zhu, 2017) Electronic nose * * (Campagnoli et al., 2011;Jia et al., 2019;Mohd Ali et al., 2020;Tognon et al., 2015;Wilson, 2013;Wu et al., 2013;Zhang & Wang, 2007) (Continues) (Boniecki et al., 2014;Charytanowicz et al., 2018;Du et al., 2019;Jayas & Ghosh, 2006;Narvankar et al., 2009;Neethirajan et al., 2006Neethirajan et al., , 2007Schoeman et al., 2016;Srivastava & Mishra, 2022) Thermal imaging * * * ElMasry et al., 2020;Gowda Nanje & Alagusundaram, 2013;Gowen et al., 2010;Manickavasagan et al., 2010;Vadivambal et al., 2010) Terahertzimaging * * * * * (Afsah-Hejri et al., 2020;Feng & Otani, 2021;Ge et al., 2014;Jiang et al., 2015Jiang et al., , 2019Shen et al., 2021;Tan et al., 2014) Acoustic sensors * * * (Aboonajmi et al., 2015;Amoodeh et al., 2006;Eliopoulos et al., 2015;Gasso-Tortajada et al., 2010;Mankin et al., 2021;Srivastava & Mishra, 2022) available for bread-its most common end product. ...
With the growing global population, the need for food is expected to grow tremendously in the next few decades. One of the key tools to address such growing food demand is minimizing grain losses and optimizing food processing operations. Hence, several research studies are underway to reduce grain losses/degradation at the farm (upon harvest) and later during the milling and baking processes. However, less attention has been paid to changes in grain quality between harvest and milling. This paper aims to address this knowledge gap and discusses possible strategies for preserving grain quality (for Canadian wheat in particular) during unit operations at primary, process, or terminal elevators. To this end, the importance of wheat flour quality metrics is briefly described, followed by a discussion on the effect of grain properties on such quality parameters. This work also explores how drying, storage, blending, and cleaning, as some of the common post‐harvest unit operations, could affect grain's end‐product quality. Finally, an overview of the available techniques for grain quality monitoring is provided, followed by a discussion on existing gaps and potential solutions for quality traceability throughout the wheat supply chain.
... In many countries, the price of the grain is determined by the content of protein, starch and for the hardness. There are different methodologies for evaluation of these quality parameters, such as the chemical composition and the infrared technology, which allows to determine different constituent in grains, including in online systems in the plantation itself (Maertens, Reyns, & De Baerdemaeker, 2004). Infrared spectroscopy is pointed as a fast and reliable way of investigation of the quality and safety of food (Downey, 1998). ...
... In 1980, the technique was adopted by the Federal Grain Inspection Service of the United States, and the wheat protein test by NIR was finally approved by the American Association of Cereal Chemists (AACC) in 1989 (Osborne, 2000). Several authors have described the possibility of direct measures of protein, moisture, dry weight and starch of several grains by NIR (Engel, Long, Carlson, & Reed, 1997; Maertens et al., 2004; Montes et al., 2006 ). For that, calibration models were built between the NIR spectra and the quality parameters by the PLS algorithm , with standard error of cross-validation (SECV) around 0.57 and 0.31% for protein and moisture in wheat, respectively (Maertens et al., 2004). ...
... Several authors have described the possibility of direct measures of protein, moisture, dry weight and starch of several grains by NIR (Engel, Long, Carlson, & Reed, 1997; Maertens et al., 2004; Montes et al., 2006 ). For that, calibration models were built between the NIR spectra and the quality parameters by the PLS algorithm , with standard error of cross-validation (SECV) around 0.57 and 0.31% for protein and moisture in wheat, respectively (Maertens et al., 2004). In the field of analysis of oils, MIR has been effectively used to determine the cis and trans content, iodide, saponification number, peroxide and free fatty acids in oil and fat samples (Innawong, Mallikarjunan, Irudayaraj, & Marcy, 2004; Van De Voort, Sedman, & Russin, 2001). ...
... For example, this technique was applied for on-line detecting fat, moisture, and protein content during meat processing (Isaksson et al., 1996). With respect to grains, some researchers have installed NIRS equipment in the harvester for continuous detection of parameters characterizing grain quality such as protein and moisture content (Maertens et al., 2004). These on/in-line applications have established their control capability in food processing. ...
... Engel et al. (1997) described an approach for inspecting grain protein on-line by the use of NIR analysis. On-line measurement of grain quality with respect to moisture and protein content by a NIR measurement device (Maertens et al., 2004) that was installed in a bypass unit of the clean grain elevator in a conventional combine harvester has been possible (Fig. 3). The calibration models between NIR spectra and quality parameters were developed by PLS algorithm and validated through cross-validation, with standard error of cross-validation (SECV) of 0.57% and 0.31% for protein and moisture content, respectively. ...
... Measurement configuration on the bypass of the elevator(Maertens et al., 2004). ...
Over the past 30 years, on/in-line near infrared (NIR) spectroscopy has proved to be one of the most efficient and advanced tools for continuous monitoring and controlling of process and product quality in food processing industry. A lot of work has been done in this area. This review focuses on the use of NIR spectroscopy for the on/in-line analysis of foods such as meat, fruit, grain, dairy products, beverage and other areas, and mainly looks at the literature published in the last 10 years. The topics covered emphasize the methods designed for on/in-line measurement of data, chemometric treatment, as well as interpretation of the experimental observations. Finally, problems relating to the successful applications of on/in-line NIR spectroscopy in production processes have been briefly outlined.
... Whole grain analyzers based on the near infrared (NIR) spectroscopic techniques pioneered by Norris (1964) have been developed for combine harvesters and used for continuous in-line measurement of GPC across fields (Maertens et al., 2004;Long et al., 2008). These systems are reported to be accurate in the field to within 5.7 g kg -1 GPC for winter wheat (Maertens et al., 2004), 6.6 g kg -1 for hard red spring wheat (Long and Rosenthal, 2005), 3.1 g kg -1 for soft white winter wheat (Long et al., 2008), and 4.5 g kg -1 for Australian hard spring wheat (Whelan et al., 2009). ...
... Whole grain analyzers based on the near infrared (NIR) spectroscopic techniques pioneered by Norris (1964) have been developed for combine harvesters and used for continuous in-line measurement of GPC across fields (Maertens et al., 2004;Long et al., 2008). These systems are reported to be accurate in the field to within 5.7 g kg -1 GPC for winter wheat (Maertens et al., 2004), 6.6 g kg -1 for hard red spring wheat (Long and Rosenthal, 2005), 3.1 g kg -1 for soft white winter wheat (Long et al., 2008), and 4.5 g kg -1 for Australian hard spring wheat (Whelan et al., 2009). ...
Grain segregation by grain protein concentration (GPC) may help growers maximize revenues in markets that offer protein premiums. Our objective was to develop an on-combine system for automatically segregating wheat (Triticum aestivum L.) by GPC during harvest. A multispectral optical sensor scans the grain as it is conveyed by the combine's grain bin-filling auger. Light from the optical probe is transmitted through a fiber optic cable to a spectrometer, which determines the spectral characteristics of the grain. This information is processed by the instrument control software that is programmed to calculate GPC from a chemometric model. The continuous GPC output is simultaneously fed to a binary computer algorithm for triggering a logic circuit and operating a mechanical diverter valve that diverts the grain into either one of two bins. Field tests of the system were conducted during harvest of hard red spring wheat using a Case IH 1470 combine modified with front and rear bins. Front and rear bins were compared in terms of the mean and frequency distribution of the optically sensed GPC. In addition, the grain in each bin was manually sampled and tested in the laboratory for GPC. Results showed that it is possible to use the GPC measured by an optical sensor to effectively control a mechanical diverter valve for routing the grain into one of two bins on a combine. An advantage of this approach is that prior knowledge of harvesting zones is not required.
... Usually the highest content of protein can be observed in terrains located at a high level, whereas the yield is higher in terrains located at lower levels [3, 9]. So far research projects have usually been limited to monitoring and recording the content of protein in harvested grain [7, 8, 15]. Until recently attempts to divide the stream of grain were only made in stationary conditions in grain elevators [16]. ...
... However, the authors of this study are of the opinion that the decision algorithm used for controlling the process of grain stream division, which is based only on the data obtained from the spectrometer assessing the quality of harvested grain, may be unreliable. As Maertens [8] proved, this fact may be particularly evident in the case of very dynamic variations in the parameters describing grain quality and simultaneous considerable delays of the signal due to the time of the flow of grain through the threshing and cleaning mechanisms of the combine harvester. At the same time the authors think that the likelihood of making the right decision to channel grain into one of the two chambers of the grain container in the harvester may be increased by using the information about variable environmental conditions in the direct neighbourhood of the harvester at work. ...
The aim of the study was to attempt to build and validate the neural model controlling the qualitative selection of the stream of grain mass as early as the stage of combine harvesting of winter wheat. The model uses the highest possible number of data describing locally changeable environmental conditions such as: protein content, moisture and yield of wheat grain, soil abundance in basic nutrients (total Kjeldahl nitrogen, exchangeable phosphorus and potassium, magnesium) and additionally -the pH coefficient, content of organic matter in soil and the relative altitude. The construction of the neural model was preceded with a multiple regression analysis. The results of the analysis (α = 0.05) indicated statistical significance of all of the traits under analysis, which influence grain quality and are defined as the content of protein. The MLP neural network (9-30-1) consisted of one hidden layer containing 30 neurons, one output and nine inputs. The network learning was done with the BFGS (Broyden-Fletcher-Goldfarb-Shanno) algorithm in a single phase during 827 epochs with the SOS error function. The study was a part of the development project No. R12 0073 06 entitled "Development and validation of the technology for separation grain stream during cereals selective harvesting", financed by the Polish National Centre for Research and Development.
... Usually the highest content of protein can be observed in terrains located at a high level, whereas the yield is higher in terrains located at lower levels [3, 9]. So far research projects have usually been limited to monitoring and recording the content of protein in harvested grain [7, 8, 15]. Until recently attempts to divide the stream of grain were only made in stationary conditions in granary [16]. ...
... However, the authors of this study are of the opinion that the decision algorithm used for controlling the process of grain stream division, which is based only on the data obtained from the spectrometer assessing the quality of harvested grain, may be unreliable. As Maertens [8] proved, this fact may be particularly evident in the case of very dynamic variations in the parameters describing grain quality and simultaneous considerable delays of the signal due to the time of the flow of grain through the threshing and cleaning mechanisms of the combine harvester. At the same time the authors think that the probability of making the right decision to send grain into one of the two chambers of the grain tank in the harvester may be increased by using the information about variable environmental conditions in the direct neighbourhood of the harvester at work. ...
The study presents the correlation between the quality of winter wheat grain, understood as the content of protein, and the parameters of harvested grain (moisture and yield) and locally variable environmental conditions (relative altitude, the content of total Kjeldahl nitrogen, exchangeable phosphorus and potassium, magnesium, the pH coefficient, the content of organic matter in soil). The results obtained on the basis of the data collected from 5 production fields of the total area of 112 ha give grounds for the conclusion that by application of multiple regression it is possible to construct a relatively pre-cise model for prediction of the content of protein in wheat grain even on the basis of the measurement of easily obtainable information about the relative altitude and yield. However, the effectiveness of the model will be limited to a small field ar-ea. The construction of universal model using information about locally variable environmental conditions is difficult due to the strong variability of the correlation between the analysed traits describing environmental conditions and the content of protein in wheat grain. The study was a part of the development project No. R12 0073 06 entitled "Development and validation of the technology for separation grain stream during cereals selective harvesting ", financed by the Polish Ministry of Science and Higher Education. Key words: grain quality, multiple regression, environmental conditions, selective grain harvest, VIS-NIR spectroscopy
WYKORZYSTANIE INFORMACJI O LOKALNIE ZMIENNYCH WARUNKACH ŚRODOWISKOWYCH W CELU PRZEWIDYWANIA JAKOŚCI ZIARNA PSZENICY PODCZAS ZBIORU Streszczenie W pracy przedstawiono zależności pomiędzy jakością ziarna pszenicy ozimej, rozumianą jako zawartość białka, a parame-trami zbieranego ziarna (wilgotność i wielkości plonu) oraz lokalnie zmiennymi warunkami środowiskowymi (względna wy-sokość n.p.m., zawartość azotu ogólnego, wymiennego fosforu i potasu, oraz magnezu, współczynnik pH, zawartość materii organicznej w glebie). Wyniki uzyskane w oparciu o dane zgromadzone na 5 produkcyjnych polach o łącznej powierzchni 112,78 ha pozwalają stwierdzić, że stosując regresję wieloraką można zbudować stosunkowo dokładny model do predykcji zawartości białka w ziarnie pszenicy nawet w oparciu o pomiar łatwych do pozyskania informacji o względnej wysokości n.p.m. i wielkości polonu, jednak jego skuteczność będzie ograniczona do niewielkiego obszaru powierzchni. Budowa uni-wersalnego modelu wykorzystującego informacje o lokalnie zmiennych warunkach środowiskowych jest utrudniona ze względu na silną zmienność zależności pomiędzy analizowanymi cechami opisującymi warunki środowiskowe, a zawarto-ścią białka w ziarnie pszenicy. Pracę zrealizowano w ramach projektu rozwojowego nr R12 0073 06 pt: "Opracowanie i walidacja technologii rozdziału strumienia ziarna podczas selektywnego zbioru zbóż" finansowanego przez MNiSW Słowa kluczowe: jakość ziarna, regresja wieloraka, warunki środowiskowe, selektywny zbiór zbóż, spektroskopia VIS-NIR
... A more rapid assessment of mycotoxin contamination can be done by NIR spectroscopy technology installed « on the grain flow ». Fourier transform near infrared spectroscopy (NIRS) and multidimensional statistical interpretation of spectroscopic data records are used for the determination of DON content in durum wheat "online" during handling grain at grain stores (Maertens et al., 2004;Huang et al., 2008;De Girolamo et al., 2009;Fernandez-Ibanez et al., 2009). Nevertheless, although this device is convenient for detecting DON contaminated wheat at the legal limit of tolerance fixed by European Regulations (e.g.1250 ppb in common wheat), this technology is not practically usable for the detection of very low levels of OTA or AFB1 mycotoxins for which the regulated tolerance threshold is very low (5 and 2 ppb respectively for OTA and AFB1). ...
Fungal spoilage of stored grains may occur when activity of water (aw) in cereal grain exceeds a critical limit enabling mould growth. Because it is not feasible to maintain all parts of large grain bulks below this critical moisture limit during prolonged storage time, an infection by seed-borne fungi is not rare in cereal grain stored under humid temperate or hot climates, inducing irreversible qualitative losses. Additionally, some fungal species produce harmful mycotoxins. The most harmful toxigenic species belong to the group of xerophilic species (genera Aspergillus and Penicillium). Because mycotoxin contamination of cereal grain is a worldwide issue for public health and a permanent concern for cereal-food industries facing the challenge of a permanent monitoring mycotoxin content in their primary matters, tolerable levels of mycotoxins are severely regulated worldwide. Mycotoxin-producing species growth is closely dependent of grain moisture levels enabling biological activity in grain ecosystem. Consequently, mould growth in stored grain bulks can be anticipated through early detection of grain and mould respiration. The prevention of mycotoxigenic fungi spoilage of stored grain can be managed by a preventive strategy. The main objective of the review was to describe the different methods, material and practices combined in such an integrated preventive approach. Some solutions potentially acceptable for the decontamination of moderately contaminated grain are also discussed.
... While these complex adjustments are a fine balancing act, studies to date have only focused on measurement of protein and moisture content on pre-harvest (Reyns et al., 2002;Maertens et al., 2004) or post-harvest grain quality, such as characteristic appearance (color and shape) or damage to grain (split rice or insect damaged rice) (Shigeta et al., 2007;Akiyama et al., 1996). In the case of foreign matter, many rice facilities already have pre-cleaning machines in the market to separate stones and heavy impurities from the paddy (rice and brown rice) utilizing bulk density difference. ...
A machine vision system with double lighting and an image processing algorithm were developed to detect undesirable objects in the paddy as it is being harvested to provide information for adjustment of parameter settings by the operator and automate combine harvester procedures. The system consisted of a web camera, a double lighting system (frontlight and backlight), an image acquisition, and image analysis algorithm. Images captured by this system were used for detection of undesirable objects (long rachis branches, grass and leaves, straw and stalks) and damaged grain (brown rice and cracked rice). Evaluation of the results demonstrate the proposed detection algorithm had a detection correlation of determination, R² greater than 0.70 for detection of each of the undesirable objects. Future application of this system in the field could help the operator of the combine harvester to improve the efficiency and adjustment of automated harvesting procedures, as well as the quality of the harvested paddy.
... For these material paddy grain quality characteristics, most research has either focused on pre-harvest attributes or on post-harvest attributes, particularly the milling process. Research on pre-harvest attributes has demonstrated the significant within-field variability of paddy grain quality (protein and moisture content) [1][2][3], while in the post-harvest milling process, quality assessment has focused on physical characteristics, such as immature grain, varietal purity, and discolored grain (identification of damaged grain kernels) [4], inspecting and identifying rice varieties [5], degree of milling [6], yield prediction, and percentage of whole kernels [7], etc. To date, though, no research has been done on quality assessment of the material attributes of the paddy during harvesting. ...
A machine vision system to evaluate harvested paddy grain quality during harvesting using double lighting was developed. The prototype consisted of a low-cost web camera and two lighting systems: a ring white LED for front lighting, and a flat dome white LED light for backlighting. Both lighting systems were arranged in a coaxial axis, making the system simple, compact and easy to handle. The aim of the system is to analyse the captured images and determine the amount of unwanted materials (rachis branch, grass and leaves, and stems) and damaged grain (brown and crack rice) present in the paddy as it is being harvested. In this paper, we introduce the first step in the development of the system: the design and selection of components to optimize the performance of the system to monitor harvested paddy grain quality. The idea would be to mount the system on top of the inlet channel of the grain tank of a combine harvester to provide real-time assessment of harvesting operational parameters.
... A more rapid assessment of mycotoxin contamination can be done by NIR spectroscopy technology installed « on the grain flow ». Fourier transform near infrared spectroscopy (NIRS) and multidimensional statistical interpretation of spectroscopic data records are used for the determination of DON content in durum wheat "on-line" during handling grain at grain stores (Maertens et al., 2004;Huang et al., 2008;De Girolamo et al., 2009;Fernandez-Ibanez et al., 2009). This device is convenient for detecting DON contaminated wheat at the legal limit of tolerance fixed by European Regulations (e.g. ...
IOBC/WPRS “Integrated Protection of Stored Products” Conference, Zagreb (Croatia), June 28 – July 1st, 2015
Session 3: Prevention of microflora infection and development of mycotoxins
Integrated approach of the prevention of mould spoilage risks and mycotoxin contamination of stored grain – A European perspective
Fleurat-Lessard Francis
INRA, UR 1264 – Mycology and Food Safety, C.R. INRA Bordeaux-Aquitaine, 71 avenue Edouard Bourlaux, CS 20032, F-33882 Villenave d'Ornon Cedex, France
E-mail : francis.fleurat-lessard@bordeaux.inra.fr
Abstract: Stored grain moulds may grow in cereal grain when moisture content is more than ~ 14.5% (0.65 aw) and it is easy to exceed this grain moisture critical limit during grain storage time. The invasion of seeds by seedborne fungi can induce grain heating, off-odours and reduction of germination capacity. Some species produce harmful mycotoxins. Mycotoxin contaminations of cereal grain are a worldwide issue for public health, agro-food industry concerns, and economics. The most toxigenic storage fungi include members of the genera Aspergillus and Penicillium. However, formation of mycotoxins is closely related to mould growth. Thus, the prevention of mould growth in stored grain bulks is the single way to manage this important grain quality issue.
The integrated management of storage moulds spoilage risks is based on six pillars: i/ Prevention of mould development in keeping grain condition below the critical moisture threshold enabling harmful fungus species germination and growth; ii/ Accurate monitoring of grain aw and temperature changes during all the storage period, associated to the monitoring of parameters/indicators of storage microflora respiration activity; iii/ implementing hermetic storage of grain in airtight bins and/or under modified atmospheres inhibiting mould growth (acceptable only for very high added value grain); iv/ reduction of grain bulk moistening trends by physical intervention means or by preservative additives (e.g. for grain intended for animal feed); v/ use of biocompetitive strains of fungi or bacteria to prevent the development of Aspergillus and Penicillium spp. toxigenic strains to install in grain bulks; vi/ use of physical treatments (ozone, grain peeling or abrasion) to limit mycotoxin contamination risks in processed cereal products. The main advantages and drawbacks of these components of an integrated approach of mould spoilage and mycotoxin contamination management in cereal grain during storage are presented. Future research needs on this topic are evocated.
Keywords: stored-grain ecosystem, seedborne fungi, grain spoilage, mycotoxin contamination, preventive management, deterioration indicators, integrated management.
... These "in-line" systems have the ability to rapidly and accurately measure GPC in a process stream during harvest. On-combine accuracies have been reported to be within 5.7 g kg -1 GPC for winter wheat (Maertens et al., 2004), 6.6 g kg -1 GPC for DNS wheat (Long and Rosenthal, 2005), 3.1 g kg -1 GPC for soft white winter wheat (Long et al., 2008), and 4.5 g kg -1 for Australian hard spring wheat (Whelan et al., 2009). ...
By ignoring spatial variability in grain quality, conventional harvesting systems may increase the likelihood that growers will not capture price premiums for high-quality grain found within fields. The Grain Segregation Profit Calculator was developed to calculate the cutoff value to use for segregating wheat (Triticum aestivum L.) into two lots on the combine such that the prices received for average protein levels in the two lots maximize profit. The calculator is written in Java with Microsoft Visual Studio 2010 components to allow for web-based functionality. A graphical user interface helps users input the price schedule and the mean and standard deviation of grain protein concentration of their field; the potential increase in profit from segregating the grain into two distinct lots is then calculated. The results of segregation of dark northern spring wheat were used to illustrate the calculator. Based on a 17-yr average high premium price schedule, the effect of mean protein and standard deviation on marginal returns was examined. Revenue from grain segregation was found to be sensitive to three factors within grain production: (i) the average level of a field's protein, (ii) the protein variability within a field, and (iii) premium schedules being paid in the marketplace.
... Taylor et al. 20 and Long et al. 11 succeeded in measuring wheat protein content as it was being harvested using a conventional combine (r=0.67 to 0.77 and SEP=0.65 to 0.99%) and constructed protein maps based on a transmittance sensor. Similarly, Maertens et al. 13 and Long et al. 12 mounted a reflectance device on a conventional combine and reported successfully measuring wheat protein (r=0.55 to 0.71 and SEP=0.56 to 0.90%). Corey 4 compared transmittance and reflectance sensors, with no apparent difference in measurement precision. ...
We have developed a near-infrared (NIR) spectrometer mountable on a head-feeding combine for measuring rice protein in real time while harvesting. The developed sensor employs reflectance optics instead of the more usual transmittance optics because (1) it operates under severe vibration and dust conditions; (2) it performs measurements in high moisture contents, low fluidity of rough rice; and (3) because of low light transmittance due to absorption by husks. The light source was a tungsten halogen lamp, with a diffusion cylinder installed so that uniform light would illuminate the sample. An Si-CCD measured the spectrum from 740 nm to 1140 nm with a post-dispersive grating. We made a calibration curve of brown rice protein from a spectrum of rough rice examined in a laboratory. The calibration curve accuracy was r= 0.87 and SECV (Standard Error of Cross-Validation) =0.47%. In the adopted measurement method, the sensor loaded the rough rice into a wide sample chamber by gravity and analyzed the loaded grain at the bottom using a reflected signal. The developed sensor was able to measure the protein content of brown rice from spectra of rough rice taken under severe conditions, e.g., a high-vibration, high-dust harvesting environment. In addition to the protein content, the rice weight and moisture content could be displayed on the monitoring terminal in real time. The accuracy of the protein content measurements in these field examinations was r=0.65 and SEP (Standard Error of Prediction) =0.22%. The SEP was far better than the SECV of the calibration, but the protein content fell in a narrow range in the field examination. Thus, we concluded that the actual accuracy was the same as the calibration.
... 14 Previous research has revealed a range of measurement accuracies from 3.1 g kg −1 to 6.6 g kg −1 for grain protein concentration of cereal crops with different in-line sensors. [15][16][17][18][19] Few reports have been found in the literature in which in-line NIR sensors have been used to measure the oil concentration in oilseeds. Measurement accuracies of ≤0.9% (9.0 g kg −1 ) have been achieved for oil content in rapeseed when directly measured on a combine harvester with use of diode array spectrometers. ...
Natural variation in the seed oil concentration of oilseed crops sent to a crushing plant can impair the recovery of the oil from the seed. Consequently, there is interest in applying in-line near infrared (NIR) spectroscopy to measure the oil concentration of the seed to be processed and, in using the information obtained, to maximise expeller efficiency. The objective of this study was to determine how well in-line NIR spectroscopy could determine the seed oil concentration of canola (Brassica napus L.) when there was direct contact of the sensor head with the grain stream. Reflectance spectra from 850nm to 1650nm were obtained by sliding grain samples of canola directly across the sensor head of the Polytec 1721 NIR reflectance analyser. Reference analytical results were estimated using the NIR optical spectra as regression estimators. The resulting prediction equation with eight latent variables resulted in a coefficient of determination of 0.95, standard error of cross-validation of 0.727% and relative performance determination of 4.77. Validation results, based on site or year omission, confirmed the ability of the instrument to accurately predict seed oil concentration in a grain stream. This creates opportunities for monitoring the oil content of seed entering the expeller and using this information to adjust the expeller for maximum efficiency.
... The spectrophotometer was a fibre-type Vis-NIR instrument developed by Zeiss Company (Zeiss Corona 45 visnir fibre, Germany). It is fast (1 soil scan per 0.4 s) and of small size, has no moving parts, and has been successfully used on mobile machines, e.g. to measure grain quality on combines (Reyns et al., 2001;Maertens et al., 2004), soil moisture content (Mouazen et al., 2005) and extractable P, P-avl, total carbon, organic carbon and pH . It was also used to optimise an on-line variable rate applicator of triple super phosphate (P 2 O 5 ) during maize planting based on on-line measurement of soil extractable phosphorus using the on-line Vis-NIR sensor of this study, shown in Fig. 1 (Maleki et al., 2006b. ...
On-line measurement of soil properties using the visible (Vis) and near infrared (NIR) spectroscopy is sensitive to soil-to-sensor distance (D) and angle (α) variations, which have prevented the successful development of on-line soil sensors so far. This study was undertaken to minimise these variations through optimising the three-point linkage of the tractor to improve the quality of soil spectra and the accuracy of plant available phosphorus (P-avl) measured with an on-line soil sensor. The sensor consisted of a tine, to the back of which an optical probe was attached to acquire soil spectra in diffuse reflectance mode from the bottom of the trench opened by the tine. A mobile, fibre-type, Vis–NIR spectrophotometer (Zeiss Corona 45 visnir fibre, Germany), with a measurement range of 306.5–1710.9 nm was used. Five lengths of the third point link (L) of the tractor of 545, 550, 555, 560 and 565 mm were selected to evaluate the quality of spectra collected on-line at 0.15 m tine depth. The on-line measured spectra were corrected to remove the effect of D and α. The correction was evaluated by estimating the accuracy of predicting P-avl using on-line measured spectra and a previously developed P-avl calibration model.
... During the 14 execution of tasks the files are modified and binary data appended by MICS. 15 16 Communication between FMIS and MICS is based on standardised XML data transfer files, 17 Figure 3. The task controller interface driver is responsible for sending task data to the task 18 controller in proprietary or XML format. ...
Sensors for on-the-go collection of data on soil and crop have become essential for successful implementation of precision agriculture. This paper analyses the potentials and develops general procedures for on-the-go data acquisition of soil sensors. The methods and procedures used to manage data with respect to a farm management information system (FMIS) are described. The current data communication standard for tractors and machinery in agriculture is ISO 11783, which is rather well established and has gained market acceptance. However, there are a significant number of non-ISO 11783 compliant sensors in practice. Thus, two concepts are proposed. The first concept is on-the-go data collection based on ISO 11783, which mostly covers data on parameters related to tractor and machine performance, e.g. speed, draught, fuel consumption, etc. Process data from sensors with Control Area Network (CAN) interfaces is converted into ISO 11783 XML and then imported into relational database at FMIS using RelaXML tool. There is also the export function from database to task controller (TC) to provide task management, as described in ISO 11783:10. The second concept is on-the-go data collection with non-ISO 11783 sensors. This data is likely to be recorded in many formats, which require an import service. An import service is based on local or public sharing or semantic mapping outputting a common format for FMIS (e.g. AgroXML). Import is best performed as close to the generation of sensor data as possible to maximise the availability of metadata. A case study of sensor based variable rate fertilisation (VRF) has been undertaken focussing on German fertilisation rules.
... The simultaneous modelling of fertilization and quality specific harvesting is rather complex and has not been given much attention in the scientific literature yet. The separation of harvested grains into different fractions of specific qualities could be achieved through zone harvesting (Tozer and Isbister, 2007), separation in harvesters with specific online sensors (Maertens et al., 2004) or separation at the farm (Thylén and Rosenqvist, 2002). ...
Incorrect fertilizer decisions can be costly if quality of the output, in addition to yield, is influenced by the application rate, which contrasts the flat payoff function estimated for fertilizer by previous studies focusing only on quantity. This study aims at modelling economic potentials of the combination of site-specific fertilization and quality specific harvesting at the example of wheat (Triticum aestivum L.), in Germany. Crop yield and protein response data to different nitrogen fertilizer applications were used from 15 locations to simulate site-specific wheat management. Four different management strategies were compared using a step wise price function for wheat qualities: uniform management, completely separate management, site-specific fertilization with uniform harvest, uniform fertilization with quality-specific harvest. It was found that opportunity costs (>50Â [euro]/ha) may apply, if threshold values for crop qualities are missed. Separation of different qualities can reduce this risk and create incentives for producing higher qualities on heterogeneous fields. Completely separate management had an economic advantage of up to 30Â [euro]/ha for the gross revenue, while site-specific fertilization alone had only marginal economic effects. However, these advantages have to cover costs for the use of technologies used, to be economically preferable.
... The advantages of spectroscopic method over traditional methods such as chemical analysis include ease of use, rapid and continuous monitoring capabilities (appropriate for online monitoring), cost-effectiveness, reliability, nondestructive nature, and ability to quantify multiple nutrients simultaneously even in high-moisture foods (Huang et al., 2008;Osborne, 2000). Therefore, NIRS in combination with chemometrics has been researched for estimating quality of meat and grain products during handling and processing (Park et al., 1998;Maertens et al., 2004). In meat products, NIRS has been used for sensory analysis and nutrient labeling (Ortiz-Somovilla et al., 2007;Uddin et al., 2006;Park et al., 1998;Osborne, 1981). ...
... IR, make the region eminently suitable for the analysis of samples with a high content of water such as foods and beverages. 4,5 In the brewery industries, NIR spectroscopy is intensely applied to the quality control of raw materials 6,7 and intermediate and end products. 8,9 However, despite the many instrumental and spectroscopic advantages, there are some issues surrounding the use of NIR in process monitoring and control in food production. ...
Some of the practical aspects of long-term calibration-set building are presented in this study. A calibration model able to predict the Kolbach index for brewing malt is defined, and four different validations and resampling schemes were applied to determine its real predictive power. The results obtained demonstrated that one single performance criterion might be not sufficient and can lead to over- or underestimation of the model quality. Comparing a simple leave-one-sample-out cross-validation (CV) with two more challenging CVs with leave-N-samples-out, where the resamplings were repeated 200 times, it is demonstrated that the error of prediction value has an uncertainty, and these values change according to the type and the number of validation samples. Then, two kinds of test-set validations were applied, using data blocks based on the sample collection's year, demonstrating that it is necessary to consider long-term effects on NIR calibrations and to be conservative in the number of factors selected. The conclusion is that one should be modest in reporting the prediction error because it changes according to the type of validation used to estimate it and it is necessary to consider the long-term effects.
... Development of a cost-effective way to segregate high from low quality wheat would increase grain quality consistency and potentially add value to crop that is currently marketed as a low-value, bulk commodity. Although various segregation approaches have been suggested (Baker et al., 1999;Arizmendi and Herrman, 2003;Maertens et al., 2004;Long et al., 2008), perhaps the system with the greatest potential for segregating wheat by quality is separation by kernel density. Low-density, pinched, shriveled kernels have a lower endosperm to bran and germ ratio as compared to high-density, sound kernels. ...
Development of a cost-effective way to segregate soft white wheat (Triticum aestivum L.) by quality would add value to a product that is currently marketed as a low-value commodity. Segregating grain by kernel density for improved quality is a technique that holds promise, but further research is needed. To address this, a study was initiated to determine the relationship of kernel density and soft white wheat quality in terms of test weight, protein content, milling performance, and end-use characteristics. The study was conducted in northeastern Oregon using Stephens soft white winter wheat samples collected from fields representing three different cropping systems over two crop years. Non-separated samples, samples that had been passed over a gravity table once and separated into four density fractions, and samples that had been passed over a gravity table twice and segregated into seven density fractions were analyzed for kernel density and quality characteristics. Correlations between quality characteristics and kernel density ranged from poor to high (r 2 = 0.00 to 0.82) for non-segregated samples, but improved as the sample became more homogenous through segregation by density using a gravity table. For the samples that had been passed over a gravity table twice, wheat quality characteristics of test weight, protein content, milling score, mixograph absorption, and cookie diameter were highly correlated with kernel density (r 2 = 0.94 to 0.95). Break flour yield was also highly correlated with kernel density (r 2 = 0.89). When sets of data for samples that had been passed over the gravity table once and twice were analyzed collectively, correlations between quality characteristics and kernel density were similar, but slightly lower (r 2 = 0.88 to 0.94). Quality scores calculated from these data and used to evaluate overall grain, milling, end-use, and overall wheat quality were also highly correlated with kernel density (r 2 = 0.91 to 0.96). It was concluded that for homogeneous samples of one variety of soft white wheat, kernel density is an excellent indicator of wheat quality. Additional research is needed to determine if this result extends across multiple cultivars of wheat and additional crop years. Analysis of grain segregated into four density fractions showed that there were significant differences in wheat quality between the lowest density fraction, the highest density fraction, and the non-separated sample. These results further indicate that density segregation is effective for separating wheat by quality and were the impetus for the proposal of a new wheat classification system that uses overall wheat quality as the basis for determining grade. Such a system would provide a marketing advantage since wheat grade would better reflect grain value.
... In maize breeding, Welle et al. (2005) reported only moisture from an 'on-the-go' application. While wheat protein has also been estimated in a commercial harvester (Long et al., 2008;Maertens et al., 2004b), to date, only one study has shown any application of NIR 'on-the-go' assessment for estimated traits in a breeding trial situation (Welle et al., 2007). These authors showed considerable success when a NIR system was mounted on a plot harvester for canola breeding trials, where moisture, protein, oil and glucosinolates were estimated. ...
In this study, we investigated the application of “on-the-go” assessment of wheat protein and moisture under a breeding trial situation.
... The quality related price structure can create the incentive to separate the harvested grains into different fractions of specific qualities. This could be achieved through zone harvesting [9], separation in harvesters with specific online sensors [5] or separation at the farm [8]. To date only few studies have analysed the combined effect of site-specific fertilization on crop yield and crop quality [6]. ...
The paper analyzes site-specific and uniform management options for wheat production with respect to grain quality. Besides site-specific fertilization the economic potential of segregation of different grain qualities is the subject of this paper. Yield and quality response to fertilizer were taken from field experiments in Germany to calculate site-specific response functions. The economic optima were calculated for uniform management (UM), complete separate management of the subfields (SM), site-specific fertilization (SSF) and grain segregation (GS) for different price structures according to different grain qualities. The results show that over all price structures, highest economic potential was found with SM or SSF compared to UM. However, these management practices require the possibility to separately manage subfields (SM) or specific fertilization equipment and fertilizer algorithms (SSM). GS did not have a higher economic potential than UM. However, if required grain qualities are not met for the whole field, GS can substantially reduce profit losses by separating part of the grains and selling them at higher prices. This may save the farmer more than 50 € ha–1. In situations where higher grain qualities could only be obtained at the expense of yield penalties, premiums for higher grain qualities can create incentives for fertilizer rates beyond the yield maximizing rate. GS technologies may even boost this effect.
A combine harvester provides unique capabilities as a mobile sensing platform. This chapter aims to contribute to the advancement of on-combine sensor use for obtaining site-specific crop data by trying to convince potential users in the agricultural community of its value and accessibility. Today, mass/volume flow and electrical capacitance sensors are widely used for measuring grain yield and moisture. A variety of other sensors have been used in crop analysis and process control that include photoelectronic spectrometers for analysis of crop quality attributes as well as ultrasonic and laser sensors for quantifying aboveground biomass. Applications of this information include precision N management, post-harvest assessment of crop stress, grain segregation by protein concentration and mapping of late-season weed infestations. Barriers challenging wider adoption of on-combine sensing techniques include the need for (i) software for exploring multi-year yield data and constructing profit zones, (ii) inexpensive spectrometers for grain quality measurement and mapping, (iii) commercial firms offering services in spectroscopy, custom mapping and data fusion, (iv) stand-alone units with user interface and firmware for multi-sensor data collection, and (v) field studies demonstrating economic benefits of various applications of information from on-combine sensing.
As inherent spatial variability of soil phosphorus (P) within a field is usually considerable, variable rate (VR) technology for P fertilisation should be implemented over small areas. The objective of this study was to develop and evaluate a soil sensor-based VR fertilisation system for on-the-go phosphate application. The maize plant density, the number of plant leaves and yield were considered as growth indices, while comparing VR and uniform rate (UR) treatment. A total average phosphate rate of 28.24 kg ha-1 was applied on VR plots which was 1.76 kg ha-1 less than the UR (30 kg ha- 1) recommended by the standard soil test. No significant difference was observed in plant density and the number of plant leaves while comparing these two treatments. However, the yield for VR treatment was significantly higher than for UR treatment.
This chapter discusses image-preprocessing techniques. It presents the methods and materials, which can be used for hyperspectral image wavelength calibration. It discusses radiometric reflectance/transmittance calibration, including calibration to percentage reflectance, relative reflectance calibration, calibration of hyperspectral transmittance data, and spectral normalization. It focuses on noise reduction and removal. It reveals techniques such as dark current removal, spectral low pass filter, Savitzky-Golay filtering, noisy band removal, and minimum noise fraction transformation. The need for spectral preprocessing and calibration of image data is due to the fact that hyperspectral imaging systems are an integration of many different optical and electronic components. Such systems generally require correction of systematic defects or undesirable sensor characteristics before performing reliable data analysis. The main goals for calibration include wavelength alignment and assignment, converting from radiance values received at the sensor to reflectance values of the target surface, and removal and reduction of random sensor noise. The cost, time, and complexity associated with each preprocessing technique and calibration method vary significantly. It is the user's decision to choose the right spectral preprocessing method or a combination of methods to respond to the needs of each food safety and food security application.
This chapter reviews the theory of near- and mid-infrared spectroscopy. Advances in infrared spectroscopy instrumentation which will facilitate the transfer of this technology from laboratory to on-line application are discussed. The challenges associated with employing infrared spectroscopy as a process analytical technology (PAT) tool is also outlined. Finally, current and emerging application of the technology in such a role is presented. This includes applications in the fields of dairy, cereal grains and seeds, fruit and vegetables and meat and poultry.
Field spatial distribution of yield and protein content, and their interactions with soil and nitrogen (N) fertilisation were studied in an experiment on durum wheat in NE Italy. Using real-time sensors on a combine harvester, it was possible to investigate differences induced by late N foliar spray and nitrogen variable rate applications, and to verify the feasibility of implementing precision harvesting (i.e. harvesting different zones individually to increase grain quality). N fertilisation was ineffective on crop yield since water stress in the last part of the cycle hindered crop growth but had a significant effect on grain protein content. The marked spatial variability observed in the field suggests that site-specific harvesting could be a viable technique to increase wheat quality in the future.
An on-line near infrared analytical system was developed for on-line
process analysis applications. The system includes a near infrared
spectroscopy analyzer and accessories (fibers, flow cell, diffuse
reflection probes etc.). Its features and configuration were described
in detail. Good performance was acquired: the wavelength range,
resolution and stray light of spectra are 1000nm~2500nm, less than 5nm
and less than 0.1%, respectively; the repeatability and uncertainty
tolerances of wavelength are less than 0.02nm and 0.1nm, respectively;
the temperature-induced drift of wavelength is less than 0.05nm/°C
the slope and intercept ranges are 0.98~1.00 and -0.02~0.01 for
photometric linearity, respectively; spectrophotometric noise is
1.3×10-5 (RMS) for high-light flux and
7.0×10-5 (RMS) for low-light flux. The above
performance parameters of the analyzer meet and exceed the requirement
of USP1119. Furthermore, the analyzer has been successfully applied to
the laboratory, petrochemical factory and sugar mill. A Partial Least
Squares (PLS) calibration model constructed from laboratory and factory
samples was used for these applications. The application result reveals
that the analyzer can meet all requirements. All results demonstrate
that the analyzer has the merits of fast time response, excellent
modeling, high accuracy and low maintenance cost and can deal with
complex industrial environment.
Accurately measuring and understanding the fine-scale relationship between wheat grain yield (GY) and the concomitant grain protein concentration (GPC) should provide valuable information to improve the management of nitrogen inputs. Here, GPC and GY were monitored on-harvester for three seasons across 27 paddocks on an Australian farming enterprise using two independent, on-the-go sensing systems. A Zeltex Accuharvest measured GPC (%) and a John Deere GreenStar system measured GY (t/ha). Local calibration in each season for Australian spring wheat significantly improved the prediction accuracy, precision, and bias of the Zeltex Accuharvest when compared with the initial factory calibration. Substantial variation in GPC and GY was recorded at the field scale, with the least variation recorded in both parameters in the wetter season. GY (CV = 38%) was twice as variable on average as GPC (CV = 19%) across the enterprise. At this enterprise scale, a negative correlation between GPC and GY was observed for a composite of the field data from all seasons (r = -0.48); however, at the within-field scale the relationship was shown to vary from positive (max. = +0.41) to negative (min. = -0.65). Spatial variation in GPC and GY at the within-field scale was described best in the majority of cases by an exponential semivariogram model. Within-field spatial variability in GPC is more strongly autocorrelated than GY but on average they share a similar autocorrelated range (a' = similar to 190 m). This spatial variability in GPC and GY gave rise to local spatial variation in the correlation between GPC and GY, with 85% of the fields registering regions of significant negative correlations (P < 0.01) and significant positive correlations observed in 70% of fields. The spatial pattern in these regions of significantly different correlations is shown to display spatial coherence from which inferences regarding the relative availability of soil nitrogen and moisture are suggested. The results point to the suitability of these on-the-go sensors for use in more sophisticated agronomic and environmentally targeted nitrogen-use analysis.
This study was undertaken to optimise the position of an “on-the-go” soil sensor installed on a variable rate (VR) granular fertiliser application system. Optimisation was carried out by measuring the lag time between acquiring the information on soil phosphorus (P) from the soil sensor and changing the fertiliser rate on the fertilisation system. The sensing system consisted of a soil sensor coupled with a Ziess spectrophotometer (CORONA fibre VISNIR 1.7). The applicator was a four-row planter-applicator from AMAZONE (ED302). The overall lag time consisted of a series of sub lag times consisting of the times required to collect soil spectra, compile data, change the fertiliser rate and the travel time of granules from the fertiliser metering devices to furrow openers. The static and dynamic tests were carried out to measure these lag times. For the dynamic test, a hydraulic shaker (Four Poster Vibratory Stands) was used to simulate the working movement under field conditions. The results revealed that the important factor for positioning the soil sensor at the front of the “on-the-go” fertiliser application system is tractor speed. The results showed that the soil sensor should be installed at the front of the tractor and an extra time delay should be artificially included.
As the inherent spatial variability of soil phosphorus (P) within a field is considerable, variable-rate (VR) technology of P fertilisation should be implemented over small areas (e.g. 1 m2). The objective of this study was to design and implement a soil sensor-based VR fertilisation system for on-the-go application of phosphate (P2O5) during maize planting. An on-the-go visible (VIS) and near-infrared (NIR) soil sensor with a measurement range of 305–1711 nm was installed at the front of a planter-applicator for on-the-go measurement of soil P. A previously developed VIS–NIR model was used to predict the extractable phosphorous (P-ext) and a custom-built LabVIEW programme was developed to record soil spectra, predict soil P-ext, calculate phosphate during on-the-go measurement and provide the signal to the fertiliser applicator to adjust the application rate. Alternate plots were used for VR application and for uniform-rate (UR) treatment. The number of plant leaves and grain yield were measured as growth indices that may be influenced by P deficiency.The coefficient of variation (CV) of P-ext measured on-the-go ranged from 5% to 51% while variation of phosphate ranged from 36% to 76% over the experimental plots. The average phosphate applied on VR plots was 28.75, 1.25 kg ha−1 less than the UR (30 kg ha−1) recommended according to the standard soil test. The application rate of the phosphate ranged from 0 to 100 kg ha−1 in the VR plots. Lower variation in plant leaves was observed in plots with VR treatment, possibly indicating better P distribution over the VR plots. The number of plant leaves variations were 25% and 31% for VR and UR plots, respectively. However, there was no significant difference between VR and UR plots. The maize yield was significantly higher (336 kg ha−1) and less variable on plots that received VR treatment.
Subtle differences in the relationship between wavelength and pixel on photodiode array spectrometers contribute to difficulties in transferring calibrations from one instrument to another and may even introduce errors on a single instrument over time. To quantify the level of drift that might be expected in photodiode instruments, we calibrated the wavelength scale of two Zeiss MMS-1 photodiode spectrometers weekly over a 12-month period. We found no evidence of drift in the wavelength calibration. The wavelength calibration was consistent within 0.03 nm over at least 150 days and better than 0.1 nm over the year. To provide context for the wavelength accuracy, we applied small perturbations to wavelength in two partial least squares (PLS) models. We found that wavelength perturbations introduced a linear increase in bias of about 7%/nm (for example, a 1-nm perturbation shifted fruit dry matter prediction from 14% to 21%) in a kiwifruit dry-matter model and about 3.6 °C/nm in an Intralipid temperature model. By including small wavelength perturbations in the training sets, we were able to reduce this error to less than 1.7%/nm and 0.2 °C/nm in the dry-matter and temperature models, respectively. These results suggest that the wavelength scale of photodiode instruments can be very stable. However, in light of the high sensitivity of the PLS models we examined, we recommend testing and, where possible, mitigating the sensitivity of PLS models to small wavelength shifts.
The advent of near infrared (NIR) on-combine sensors gives growers the opportunity to measure the grain protein concentration of wheat (Triticum aestivum L.) during harvest. A study consisting of three sequential experiments (laboratory bench, combine test stand, and field) was conducted to evaluate the performance of an in-line, NIR reflectance spectrometer, referred to as the ProSpectra Grain Analyzer, possessing a factory calibration model. In the laboratory bench experiment, the instrument was mounted to a circulating impeller apparatus designed to simulate a moving stream of grain. The ProSpectra performed well on a validation set of 231 grain samples of soft white winter wheat and explained a high level of protein variability (R2 = 0.91, SEP = 3.1 g kg-1) with a slope near unity. In the second experiment, the sensor was installed on a combine test stand constructed from the cross and exit augers, and clean grain elevator of a combine, to create the grain flow conditions found on a combine. Predicted protein was highly correlated (R2 = 0.93, SEP = 4.5 g kg-1) with reference protein of nine large (14-kg) wheat samples. During the third experiment, the instrument was placed on the exit auger of a Case IH 1470 combine for the harvest of a 17-ha winter wheat field. Prospectra protein predictions correlated well with reference protein measurements (R2 = 0.94, SEP = 3.1 g kg-1). This study demonstrated the feasibility of using in-line NIR reflectance spectroscopy to rapidly (0.5 Hz measurement rate) and accurately (SEP < 5.0 g g-1) measure wheat protein in a moving grain stream.
Increasing safety events caused by products/food quality problems has caught more and more attention, therefore non-destructive fast detection of products/food quality becomes necessary. At present, fast detection methods commonly used include chemical colorimetric method, near infrared spectroscopy (NIRS) technique, immunoassay method, bio-sensor technique, biomicroarray method, bioluminescence method, and so on. NIRS technique has found wide application in products/food quality detection because of its characteristics such as very high speed, no sample preparation, non-destruction, no pollution, low cost etc. Many research works have been done on products/food quality detection using NIRS technique both home and abroad, from static laboratory investigations to online investigations. In the present paper, basic knowledge of NIRS and its analysis process were briefly introduced. The applications of NIRS technique in online quality detection and control of fruit, fish, meat, milk, grain, fermentation of cheese and alcohol etc were reviewed. Finally, the existing problems of NIRS were pointed out and the prospect of NIRS technique was discussed. In the future, NIRS technique will combine with network technique to realize online update and upgrade of NIR models. And spectral imaging technique will be the development trend of NIRS technique in the 21st century.
This research was undertaken to quantify the structure of protein variation in a commercial hard red winter (HRW) wheat (Triticum aestivum L.) production system. This information will augment our knowledge and practices of sampling, segregating, marketing, and varietal development to improve uniformity and end-use quality of HRW wheat. The allocation of kernel protein variance to specific components in southwestern Kansas was performed by a hierarchical sampling design. Sources of variability included Field, Plot (plots within a field), Row (rows within a plot), Plant (plants within a row), Head (heads within a plant), Position (spikelets at a specific position on a head), Spikelet (spikelets within a position), and Kernel (kernels within a spikelet). Individual kernels (10 152) were collected from 46 fields planted to one of four cultivars: Jagger, 2137, Ike, or TAM 107. Kernels were evaluated for protein concentration by a single kernel characterization system equipped with a diode array near-infrared (NIR) spectrometer. For the cultivars 2137 and Ike, all sources of variability except Spikelet were statistically significant (P < 0.05). For Jagger, all sources except Row were significant and for TAM 107, variation attributed to Field and Plant were not significant. Field and Plot sources of variability contributed the greatest amount of variance within the hierarchy for Jagger, 2137, and Ike. For TAM 107, Plot was the greatest source of variability. The least squares means were calculated for the fixed effect Position. Jagger, Ike, and 2137 showed a significant protein gradient in which the highest protein concentration occurred at the base of the head and the lowest protein content at the top. For TAM 107, the greatest protein content was found at the base. Results of this study provide a benchmark for future efforts to improve wheat consistency through breeding and crop management. The protein variance structure described during this study also defines practical limits for managing and marketing protein content in HRW.
A dynamic grain flow model describes how the grain flow at the end of the threshing process reacts to feedrate variations during harvest. These input flow variations have different origins as there are variations in travel speed, cutting width and locally variable grain yield. In this study, an analysis of the model is performed and a specific application in the domain of precision farming is illustrated.A closer look at the construction of the analytical grain flow model reveals some complicated dynamics in the system and an internal return loop with a significant time delay. Especially, the latter property makes it difficult to simplify the model into a linear transfer function, an interesting form for further applications. Due to a specific property of grain spreading in the return flow, it is possible to compensate for the variations in the return flow fraction. The grain smoothing effect of the combine harvester for 0% return flow was approximated by a fourth-order linear transfer function.Some producers of combine harvesters already possess a commercial product for online grain yield measurement. These sensors are typically mounted at the end of the threshing process since this is the only place where a flow of clean grain is present. Nevertheless, this position has also a drawback. Grain flow signals, measured at the end of the threshing process contain unconditionally the influence of disturbing machine dynamics. In practice, this means that feedrate variations and subsequently estimated yield variations, are smoothed reducing the economic interest of the resultant yield maps. The inverse simplified linear transfer function makes it possible to reduce these latter errors and to improve immediately the results of further site-specific grain yield analysis.
Site specific information was acquired on a Belgian field during harvest in two consecutive years. Grain yield was recorded with previously developed sensors. At the same time, grain protein and moisture content were determined at different locations in the field. The first aim of this study was the mapping of grain moisture and protein content in order to determine the amount of variation within one field. Secondly, yield and quality maps were compared to each other and studied for relationships between the different parameters for different parts (growing conditions) of the field. Finally, these relationships were used to delineate different fertilisation zones in the field. A variation in moisture content of over 7% existed in the field in both years. Also for the protein content a variation of more then 4% was noticed. Variation in protein content varied from one year to another, possibly due to different weather conditions. Consequently, only 2% of the area of the field could be classified being over-fertilised.