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Lettuce Leaf Necrotic and Chlorotic Surface Defect Assessment Using Recurrent Neural Network Optimized by Electromagnetism-like Mechanism
Abstract
Detection of plant stress is crucial to improve cultivation management. This study presents a non-destructive solution in detecting lettuce crop health and quantitatively assessing the necrotic and chlorotic leaf surface defects due to drought-based senescence. A total of 210 matured lettuce images were collected over a week of water stressed testing using digital camera. Crop quality was classified into healthy and defective based on its canopy visual appearance using deep transfer image networks in which InceptionV3 bested other networks with accuracy of 97.321%. Necrotic and chlorotic regions of defective canopy were separately segmented using CIELab color space thresholding and extracted with color, texture, and morphological properties. Hybrid neighborhood component analysis and ReliefF confirmed that texture features are highly significant than colors for this problem. Artificial neurons on the three hidden layers of recurrent neural network (RNN) were fine-tuned using electromagnetism-like mechanism (EM). Combined EM-RNN exhibited the best R2 performances of 0.9796 and 0.9565 in predicting necrotic and chlorotic surface defect percentages respectively. Necrosis has faster spread factor of 45.3419% than chlorosis in weekly basis per canopy. This developed comprehensive model of InceptionV3-EM-RNN is an objective, reliable and quantitative approach in providing quality assessment on leaf surface defect phenotyping.
... Sustainability is the capacity of providing the current necessity without degrading the resources of future generations [19]. The application of the developed and enhanced concepts of permaculture covers and solved a wide array of problems that the world is facing, and the most prevalent one is the establishment and sustainability of food production, especially in the urban and peri-urban setting [36,37]. Ranging from home gardens to urban farms, various technologies, traditional knowledge, and techniques are being utilized as the primary goal; supplementing and expanding food production in every household or the community must be achieved while still observing ethical and diversified food production processes in the long run [38]. ...
... The United Nations Agenda 2030 includes several objectives reflected in the 17 several SDGs of which the following is related to permaculture: SDG 2 (zero hunger), 6 (clean water and sanitation), 11 (sustainable cities and communities), and 12 (responsible consumption and production) [36][37][38]. Following permaculture practices allows the farmers to have sustainable water management and irrigation systems primarily attributed to the conservation of the agricultural landscape in a particular locale [21]. ...
... Moreover, CEA improves profitability through long-term usage of the same set of tools and grow bed, and because of an adaptive system that injects nutrients to the system only when needed by crops [22]. Automatic detection of fruit and leaf diseases is also recommended [34][35][36][37]. The research, technology development, and community engagement sectors including the graduate students and academe should be the prime movers of this directive in combatting permaculture-related issues. ...
Permaculture is a land management and regenerative agriculture that is the integration of technological advancement and the natural agricultural ecosystems. It is usually manifested in green architecture and balcony gardens in an urban area. However, peri-urban geographical regions are those situated in between urban and rural areas that are experiencing the impacts of modernizing community and conventional farming. This study discusses the technological drivers making intelligent permaculture ecosystems and applications, the current issues and challenges in this emerging agricultural and horticultural scheme, and a set of future directives in intelligent permaculture ecosystems in a tropical and maritime climatic setting. Based on the systematic analysis, this study had established a guideline that compliance to UN sustainable development goals, controlled environment engineering, and planting of fruit-bearing trees and vegetables in sidewalks can solve the issues of public knowledge, ecological stability and environmental sustainability, profitability issue, and technological constraints concerning intelligent permaculture systems.
... Classical methods in measuring morphological traits of crops are through vernier caliper for length and width of root and shoot systems [32], standard color code or template [33], and digital balance for measuring weight [34], scanning electron microscopy (SEM) [35] and fluorescence imaging [36] was used to visualize the morpho-anatomical signatures of plants. Factors affecting traits include light intensity and spectrum, environment temperature and humidity, and weight of other crops stored in the vicinity of a specific crop [32][33][34][35][36]. ...
... Classical methods in measuring morphological traits of crops are through vernier caliper for length and width of root and shoot systems [32], standard color code or template [33], and digital balance for measuring weight [34], scanning electron microscopy (SEM) [35] and fluorescence imaging [36] was used to visualize the morpho-anatomical signatures of plants. Factors affecting traits include light intensity and spectrum, environment temperature and humidity, and weight of other crops stored in the vicinity of a specific crop [32][33][34][35][36]. By using computer vision tools such as the OpenCV and MATLAB color threshold the process of quantifying the abovementioned morphological traits can be done easily. ...
... It should be taken into strict consideration that the use of color space is dependent on the fruit and vegetable images. In fact, instead of using one set of color space, using neighborhood component analysis (NCA), it was confirmed 978-1-6654-0167-8/21/$31.00 ©2021 IEEE that combining color space components, for example, red, Cb, and hue components accurately and sensitively segments another variant of lettuce [32][33][34]. In the perspective of computer vision, there is a great necessity to segment properly down to the minute details and remove outliers in the image such as small binary or masked RGB pixels in the outline that was retained because of chromatic aberration problem. ...
Agricultural production system does not end with the actual harvesting of crops rather it extends to the postharvest system which primarily consists of crop storing, marketing, and transportation. However, temperature and humidity directly affect the quality of stored agricultural products. In a tropical country like the Philippines, tomato, lettuce, and other thin-skinned and highly moist crops degrade its quality and experience shape deformation over time. This study is a thematic taxonomy of intelligent postharvest storage systems discussing the techniques in the phenotyping of agricultural produce and emerging needs, trends in computer-vision-based postharvest systems, integration of artificial intelligence in postharvest systems, the current issues, challenges, and corresponding future directives in intelligent storage systems. Based on the systematic analysis, technical modeling of the storage system and postharvest crop quality grading are the emerging challenges in effectively storing crops for human consumption. It was found out that non-invasive high throughput methods for evaluation of quality and shelf life are needed. This can be done through vision-based fruit and vegetable quality grading and vision-based adaptive controls in the storage chamber. Overall, computer vision allied with artificial intelligence can make an intelligent postharvest storage system that is sustainable, profitable, and easy to implement.
... Sustainability is the capacity of providing the current necessity without degrading the resources of future generations [19]. The application of the developed and enhanced concepts of permaculture covers and solved a wide array of problems that the world is facing, and the most prevalent one is the establishment and sustainability of food production, especially in the urban and peri-urban setting [36,37]. Ranging from home gardens to urban farms, various technologies, traditional knowledge, and techniques are being utilized as the primary goal; supplementing and expanding food production in every household or the community must be achieved while still observing ethical and diversified food production processes in the long run [38]. ...
... The United Nations Agenda 2030 includes several objectives reflected in the 17 several SDGs of which the following is related to permaculture: SDG 2 (zero hunger), 6 (clean water and sanitation), 11 (sustainable cities and communities), and 12 (responsible consumption and production) [36][37][38]. Following permaculture practices allows the farmers to have sustainable water management and irrigation systems primarily attributed to the conservation of the agricultural landscape in a particular locale [21]. ...
... Moreover, CEA improves profitability through long-term usage of the same set of tools and grow bed, and because of an adaptive system that injects nutrients to the system only when needed by crops [22]. Automatic detection of fruit and leaf diseases is also recommended [34][35][36][37]. The research, technology development, and community engagement sectors including the graduate students and academe should be the prime movers of this directive in combatting permaculture-related issues. ...
Permaculture is a land management and regenerative agriculture that is the integration of technological advancement and the natural agricultural ecosystems. It is usually manifested in green architecture and balcony gardens in an urban area. However, peri-urban geographical regions are those situated in between urban and rural areas that are experiencing the impacts of modernizing community and conventional farming. This study discusses the technological drivers making intelligent permaculture ecosystems and applications, the current issues and challenges in this emerging agricultural and horticultural scheme, and a set of future directives in intelligent permaculture ecosystems in a tropical and maritime climatic setting. Based on the systematic analysis, this study had established a guideline that compliance to UN sustainable development goals, controlled environment engineering, and planting of fruit-bearing trees and vegetables in sidewalks can solve the issues of public knowledge, ecological stability and environmental sustainability, profitability issue, and technological constraints concerning intelligent permaculture systems.
... RTree is built through binary recursive partitioning [16] and optimized by using a minimum leaf size of 1 (Fig. 3a). GPR is a non-linear prediction algorithm that searches for the Gaussian distribution over the output using prior covariances [17]. It was optimized using a sigma value of 0.035985 with squared exponential kernel function, constant basis function, a beta of -60.2984, exact predict and fit methods and random active set method (Fig. 3b). ...
Coating the metallic antenna dipole can improve its mechanical qualities. However, there is a need to determine a new way to test material rigidity that is quick, minimally destructive, and eliminates costly rework. To address this emerging challenge, this study has developed a flexure indentation rigidity measurement network (FIRMnet) using soft computing for capacitively coupled antenna dipoles that can predict the hardness indention as a function of the coating material property: elastic modulus, Poisson ratio, and thickness; and load antenna weight. Here, the rigidity of coating materials is defined by surface deformation. The five most used coating materials were selected, namely acrylic, epoxy, nylon 101, high-density polyethylene, and polyvinyl chloride. Using SolidWorks, an antenna dipole plate was plotted and employed with non-linear mechanical finite element analysis through a 120° titanium cone indenter with a slightly rounded point of 0.5-inch diameter and a constant compressive force of 200 N at the center. The coating materials and thickness were varied, resulting in a total of 100 combinations. Four soft computing models were developed and optimized from which multigene symbolic regression genetic programming (MSRGP) has surpassed the performances of RTree, GPR, and RSVM in terms of accuracy (99.8%), RMSE (0.250), MAE (0.094), and inference time (0.616 s) making it the model with the least computational cost. Hence, the 10-gene MSRGP is considered the FIRMnet. Based on the findings, epoxy is the recommended material for this application. This developed approach can be retrained with a more comprehensive material and indention dataset. © 2022, School of Electrical Engineering and Informatics. All rights reserved.
... Ilocos Region, Central Luzon, and Cagayan Valley have consistently been named as top producers of tomatoes in the country, but one major problem is the postharvest storage system and the logistics to transport this product to the mainland for distribution and consumption [1]. Pre-harvest factor including fertilizer application, pruning, maturity stage, cultivar type, and irrigation affects the postharvest quality of tomatoes [2][3][4]. It has been confirmed that the chilling of fruits may prolong their shelf life [5,6]. ...
A food production system does not end in harvesting crops rather it extends up to the postharvest phases like storage, transporting, marketing, and consumption. Diamante Max F1, a hybrid tomato, is resistive to most diseases but it is one of the most highly perishable tomatoes because of its high moisture content and thin skin that can be easily affected by hot temperatures. In this study, a new non-invasive characterization technique for tomato fruit was developed using integrated computer vision and computational intelligence modeling. The Diamante F1 Max Philippine tomato (Lycopersicon esculentum Mill.) variety was used as fruit cultivar and exposed to two postharvest storage treatments: room temperature with 23−34∘C uncontrolled atmosphere; and cold room with constant 18∘C throughout the 14 days of storage period. Multigene genetic programming generated four predictive models of the architectural phenotypes: daily weight loss, surface area, total color difference, and tomato color index as functions of storage temperatures and periods. To say that fresh-marketed tomatoes are degrading, its surface area and total color difference should be decreasing, and its daily weight loss and total color index should be increasing. Morpho-anatomical analysis was performed to further analyze the degree of quality degradation in the pericarp, columella, placenta, seed membrane, and fruit epidermis. Pericarp cells shrink due to dehydration and locular gel around the seeds reduces manifesting weight loss. Overall, the influence of temperature and storage period should be managed right after harvesting the fresh produce to preserve its quality and prolong its shelf life for human consumption.
... Machine learning (ML) techniques including decision tree and linear discrimination analysis (LDA) were utilized in a study to assess the health condition of the grape leaf [11]. A recurrent neural network (RNN) was configured for the detection the health condition and quantify the surface defect of the lettuce leaf [25]. Optimization strategies including particle swarm optimization (PSO) and artificial bee colony (ABC) were enforced to improve the image feature parameters for the assessment of tomato leaf spot [12] and to optimize the RNN for the diseased maize leaf spot assessment [24]. ...
Plant diseases are the major cause of agricultural production losses. Visual leaf assessment based on human capability is the traditional diagnostic approach but leads to errors that contribute to losses. As a response, the analysis of rice (Oryza sativa) leaf quality through the development of a diagnostic method based on computational imaging was explored in this study. Feature-based machine learning (ML) algorithms including k-nearest neighbors (KNN), linear discrimination analysis (LDA), Naïve Bayes (NB), decision tree for classification (CT), and support vector machine (SVM) were configured for the classification of the health status of rice leaves according to the spectro-textural characteristics of image samples. A total of 17 features were extracted through the HSV color space thresholding technique which was further simplified through neighborhood-principal component analysis (NCA-PCA) selection that resulted in a four-feature vector (a*, Cr, contrast, homogeneity). Further, Deep neural networks (DNN) comprising ResNet-50, ResNet-101, GoogLeNet, MobileNetv2, and Inceptionv3 networks were used to categorize the disease variants. As a result, all ML models exhibited excellent classification performance. Additionally, the KNN model delivered an accuracy rating of 97.92% for health condition classification. Also, among the created DNN models, DNN RN50 produced a disease identification accuracy of 95.83%. With that, a vision-based diagnostic tool, OryzaNet, was established. This technique is a non-intrusive and novel approach to phenotyping and assessing the quality of rice leaves.
... A study introduced two machine learning approaches for fruit classification through wavelet entropy (WE), principal component analysis (PCA), feedforward neural network (FNN) [5]. In the studies [18,26], computer vision approach is used to extract image features such as the color, texture, and morphological properties in order to classify the growth stage of lettuce and analyze the health status of crop. Genetic algorithm (GA) and evolutionary strategy (ES) are employed to derive optimal values of parameters for the model development that analyzes photosynthetic signatures of lettuce crop [25,27]. ...
Traditional practice of classifying postharvest crops creates variability in quality assessment due to human-related limitations including individual disparities in visual recognition. As a solution, computer vision approach was adapted. This study aims to classify the native banana fruit to South and Southern Asia (Musa acuminata) using the image-based deep transfer networks of ResNet101, MobileNetV2, and InceptionV3, and machine learning algorithms, including classification tree (CTree), Naïve Bayes algorithm (NB), k-nearest neighbors (KNN) and support vector machine (SVM). A total of 1,164 images, derived from 194 banana tier subjects with different orientations were utilized. Color channel thresholding in CIELab(L*a*b*) color space was applied to extract the spectral (RGB, HSV, YCbCr, L*a*b*), textural (correlation, contrast, entropy, homogeneity, energy) and morphological (total area) features. These 18-feature vectors were further simplified into two most significant features (S and V) using combined neighborhood component analysis and principal component analysis (hybrid NCA-PCA). The length of the top middle finger of the banana tier was added to the features. The classification tree (CTree), regardless of feature set, was validated to have the best performance, with accuracy of 91.28% and inference time of 19.34seconds. In addition, NB, KNN and SVM models provided acceptable performance with 89.72%, 89.30%, and 89.36% accuracies, respectively. However, the deep transfer networks did not provide acceptable classification results (with ResNet101 having the highest accuracy of 50.01% among the networks used). Lastly, the proposed machine learning models served as a feasible approach in the postharvest classification of Musa acuminata.
... It has been created to be utilized in an assortment of rural exercises, Survey of farming climate information utilizing drones unmanned airborne vehicle use for more precise weeding. Collecting used for planting and nursery plants and so forth [15,[23][24][25][26][27][28][29]. ...
... An application was also generated utilizing deep convolutional NN for monitoring the growth speed of greenhouse lettuce. It uses the Mask RCNN structure that used the NFT system [35]. In general, these growth monitoring systems apply CV methodologies and computational intelligence in predicting crop properties using segmented images [36]. ...
... It is usually combined with computational intelligence (CI) which is a subset of artificial intelligence (AI) that deals on the design and application of computational networks. The integration of CV and CI has previously been employed in analyzing lettuce spectral, textural, and morphological features in relation to abiotic stressors [18][19][20][21]. Pigment profiling involves separation, extraction, and characterization of pigments [22]. ...
Phytopigments are essential indicators of plant growth. However, current methodologies use expensive laboratory devices. In this study, a low-cost approach of lettuce leaf phytopigments profiling is employed using a consumer-grade camera and integrated computational intelligence via paper chromatography. Hybrid neighborhood component analysis and ReliefF selected the blue reflectance extracted from chromatography to have the most significant impact with other leaf biophysical signatures. Chl b exhibits more complex reflectance spectrum than other pigments and considered as strong indicator of energy absorbance variations. Xanthophyll and carotenoid have the strongest and weakest retardation factor and retention time, respectively. Chl a-b has weak affinity to acetone and their average blue reflectance is confirmed to absorb the highest number of photons in white light cultivation. Leaf absorbance varies by ±1307.04 μmol m −2 s −1 PPFD per ±0.1 of blue reflectance. Among other machine learning models, Gaussian processing regression bested out multigene symbolic genetic programming and recurrent neural network in predicting the average chloroplast photosystems I and II blue reflectance with R 2 of 0.9974. This developed approach extends the application of paper chromatography from segmenting to phytopigment profiling.
Salinity is a rising concern in many lettuce-growing regions. Lettuce (Lactuca sativa L.) is sensitive to salinity, which reduces plant biomass, and causes leaf burn and early senescence. We sought to identify physiological traits important in salt tolerance that allows lettuce adaptation to high salinity while maintaining its productivity. Based on previous salinity tolerance studies, one sensitive and one tolerant genotype each was selected from crisphead, butterhead, and romaine, as well as leaf types of cultivated lettuce and its wild relative, L. serriola L. Physiological parameters were measured four weeks after transplanting two-day old seedlings into 350 mL volume pots filled with sand, hydrated with Hoagland nutrient solution and grown in a growth chamber. Salinity treatment consisted of gradually increasing concentrations of NaCl and CaCl2 from 0 mM/0 mM at the time of transplanting, to 30 mM/15 mM at the beginning of week three, and maintaining it until harvest. Across the 10 genotypes, leaf area and fresh weight decreased 0–64% and 16–67%, respectively, under salinity compared to the control. Salinity stress increased the chlorophyll index by 4–26% in the cultivated genotypes, while decreasing it by 5–14% in the two wild accessions. Tolerant lines less affected by elevated salinity were characterized by high values of the chlorophyll fluorescence parameters Fv/Fm and instantaneous photosystem II quantum yield (QY), and lower leaf transpiration.
In a survey conducted in Cannabis sativa L. (cannabis) authorized farms in Israel, plants showed disease symptoms characteristic of nutrition deprivation. Interveinal chlorosis, brittleness, and occasional necrosis were observed in older leaves. Next generation sequencing analysis of RNA extracted from symptomatic leaves revealed the presence of lettuce chlorosis virus (LCV), a crinivirus that belongs to the Closteroviridae family. The complete viral genome sequence was obtained using RT-PCR and Rapid Amplification of cDNA Ends (RACE) PCR followed by Sanger sequencing. The two LCV RNA genome segments shared 85–99% nucleotide sequence identity with LCV isolates from GenBank database. The whitefly Bemisia tabaci Middle Eastern Asia Minor1 (MEAM1) biotype transmitted the disease from symptomatic cannabis plants to un-infected ‘healthy’ cannabis, Lactuca sativa, and Catharanthus roseus plants. Shoots from symptomatic cannabis plants, used for plant propagation, constituted a primary inoculum of the disease. To the best of our knowledge, this is the first report of cannabis plant disease caused by LCV.
Seed quality is of great importance in optimizing the cost of crop establishment. Rapid and non-destructive seed quality detection methods must therefore be developed for agriculture and the seed production industry. This review focuses primarily on non-destructive techniques, namely machine vision, spectroscopy, hyperspectral imaging, soft X-ray imaging, thermal imaging and electronic nose techniques, for assessing the quality of agricultural seeds. The fundamentals of these techniques are introduced. Seed quality, including chemical composition, variety identification and classification, insect damage and disease assessment as well as seed viability and germinability of various seeds are discussed. We conclude that non-destructive techniques are accurate detection methods with great potential for seed quality assessment.
The identification of nutrient deficiency symptoms in lettuce helps both producers and technical staff to keep the plant nutritional balance in their producing areas. The objective of this study was to evaluate production and describe and record the visual symptoms caused by the isolated or combined shortage of K, Ca, B and Zn in crisphead lettuce grown in hydroponics. The experimental design was completely randomized blocks with four replications and eight treatments, representing the single (K, Ca, B, Zn) and combined (Ca and B, K and Zn, B and Zn) omission of nutrients, with a control treatment containing a complete nutrient solution. We used the crisphead lettuce cultivar Rider Plus. Under Ca shortage, plant growth was reduced and chlorosis appeared in the borders of young leaves. K shortage was the most detrimental to production of shoot fresh and dry matter and root dry matter. Where B was absent, plant growth was limited, the apical dominance was lost and leaves became wrinkled. Plants without Zn showed mild chlorosis in the blade of young leaves, elongation and bending of petioles and reduction in root density. The combined omission of Ca and B slowed down plant growth and induced necrosis at the borders of young leaves, while the combined omission of K and Zn initially induced K deficiency symptoms with reduced growth. When B and Zn were simultaneously subtracted, plants first showed B deficiency symptoms: reduction in plant size as compared to the control treatment and death of the apical bud.
Cadmium (Cd) is a metal pollutant that accumulates in cultivated soils and has detrimental consequences in terms of food safety. Lettuce (Lactuca sativa) can characterised by having a high capacity to accumulate Cd in its tissues. An analysis of Cd tolerance and Cd accumulation was carried out using two varieties of lettuce (‘Divina’ and ‘Melina’). A wide range of CdCl2 concentrations was used (0.0, 0.1, 0.6, 3.0, and 15.0 µM CdCl2). The lowest concentration (0.1 µM CdCl2) stimula’ted growth, while the two highest concentrations resulted in a reduction of the biomass production. Cadmium concentrations were found to be twice as high in roots as in shoots. ‘Divina’ displayed lower concentrations of Cd than ‘Melina’ in nearly all treatments. A strong negative correlation was observed between Cd concentrations and Cd tolerance in the roots and shoots (R² > 0.87) of both ‘Melina’ and ‘Divina’. Lettuce grown in the presence of 15.0 µM CdCl2 had leaf Cd concentrations that were 100 fold higher than the legal maximum level for vegetable products marketed for human consumption, but showed no signs of dehydration, chlorosis, or necrosis. This result is an important alert for lettuce consumers and growers.
Human exploration missions to the Moon or Mars might be helped by the development of a bioregenerative advanced life-support (ALS) system that utilizes higher plants to regenerate water, oxygen and food. In order to make bioregenerative ALS systems competitive to physiochemical life-support systems, the ' equivalent system mass ' (ESM) must be reduced by as much as possible. One method to reduce the ESM of a bioregenerative ALS system would be to deploy an automated remote sensing system within plant production modules to monitor crop productivity and disease outbreaks. The current study investigated the effects of canopy structure and imaging geometries on the efficiency of measuring the spectral reflectance of individual plants and crop canopies in a simulated ALS system. Results indicate that canopy structure, shading artefacts and imaging geometries are likely to create unique challenges in developing an automated remote sensing system for ALS modules. The cramped quarters within ALS plant growth units will create problems in collecting spectral reflectance measurements from the nadir position (i.e. directly above plant canopies) and, thus, crop canopies likely will be imaged from a diversity of orientations relative to the primary illumination source. In general, highly reflective white or polished surfaces will be used within an ALS plant growth module to maximize the stray light that is reflected onto plant canopies. Initial work suggested that these highly reflective surfaces might interfere with the collection of spectral reflectance measurements of plants, but the use of simple remote sensing algorithms such as 760/685 band ratios or normalized difference vegetation index (NDVI) images greatly reduced the effects of the reflective backgrounds. A direct comparison of 760/685 and NDVI images from canopies of lettuce, pepper and tomato plants indicated that unique models of individual plants are going to be required to properly assess the health conditions of canopies. A mixed model of all three plant species was not effective in predicting plant stress using either the 760/685 or NDVI remote sensing algorithms.
This paper proposes a new heuristic for global optimization. The method utilizes an attraction-repulsion mechanism to move the sample points towards the optimality. The proposed scheme can be used either as a stand-alone approach or as an accompanying procedure for other methods. Some test results on nonlinear test functions in the category of ``minor to moderate difficulty'' are included. The ease of implementation and flexibility of the heuristic show the potential of this new approach.
The selection of processing parameters has great influence on the work-piece quality, processing time and processing costs. However the optimization of processing parameters is a complex nonlinear constraint optimization problem which is difficult to solve by traditional methods. In order to solve this problem effectively, this paper proposes a kind of improved electromagnetism-like mechanism (EM) algorithm. First, simplify the calculation process of the power and force and improve the power and force calculation formula to speed up the convergence speed and to enhance the search ability of EM algorithm. Then improve the particle movement formula by introducing movement probability. Meanwhile the Solis & Wets search algorithm is combined with the EM algorithm to improve the local search ability. For constrained optimization problems, a processing constraints method is developed by introducing a kind of feasibility and dominance rules into the EM algorithm. This improved EM algorithm is applied in the grinding parameters optimization problem and abrasive water jet machining parameter optimization problem. The results show that the proposed method is better than other algorithms and achieves significant improvement, which verifies the feasibility and superiority of this new algorithm.
Due to a limited shelf life, baby cos lettuce lasts for only a few days after arriving at a market destination. At the present time, no research is conducted on what impacts of commercial precooling systems have on physico-chemical qualities and ultrastructure of this crop. The study provides understanding of the cellular senescence, especially associated with chloroplast and vacuole which directly affect the produce's greenness and freshness. This research was conducted to investigate the optimum parameters for vacuum cooling, and the effects of different commercial precooling systems (room cooling, forced-air cooling, and vacuum cooling) on the physico-chemical qualities, bioactive compounds and ultrastructure of baby cos lettuce during storage at 4 °C. Overall, vacuum cooling at 0.6 kPa holding pressure and 25 min holding time maintained significantly (P ≤ 0.05) better produce qualities than forced-air cooling at 1.4 m s −1 air velocity or room cooling at 0.65 m s −1. A significant increase in total phenolics content was detected in all samples, with antioxidant activity also increasing significantly until seven days of storage for samples precooled by vacuum cooling, and until nine days of storage for samples precooled by forced-air cooling, room cooling, and in the non-precooled sample, decreasing thereafter. However, both antioxidant activity and phenolics content were better retained during storage in samples pre-cooled by vacuum cooling. Although gradual declines in ascorbic acid and total chlorophyll content were observed across all treatments, cells of baby cos lettuce precooled via forced-air or room cooling and those in the control sample exhibited chlorophyll degradation (according to TEM), resulting in a diminished green colour. This study thus demonstrates that vacuum cooling is effective in prolonging the shelf life of baby cos lettuce grown in Chiang Mai, Thailand from nine to sixteen days at 4 °C and 85% RH, increasing the produce market window.
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http://authors.elsevier.com/a/1TxFg5Tbkjzmo6
Multisensory platforms for remote sensing measurements offer the possibility to monitor in real-time the crop health status without affecting the crop and environmental conditions. The concept of the speaking plant approach, and plant response based sensing in general, could be valuable providing a better understanding of the interactions between the microclimate and the physical conditions of the plants. Early detection of plant stress is critical, especially in intensive production systems, in order to minimise both acute and chronic loss of productivity. Non-contact and non-destructive sensing techniques can continuously monitor plants and enable automated sensing and control capabilities. This paper reviews past research and recent advances regarding the sensors and approaches used for crop reflectance measurements and the indices used for crop water and nutrient status detection. The most practical and effective indices are those based on ground reflectance sensors data which are evaluated in terms of their efficiency in detecting plant water status under greenhouse conditions. Some possible applications of this approach are summarised. Although crop reflectance measurements have been widely used under open field conditions, there are several factors that limit the application of reflectance measurements under greenhouse conditions. The most promising type of sensors and indices for early stress detection in greenhouse crops are presented and discussed. Future research should focus on real time data analysis and detection of plant water stress using advanced data analysis techniques and to the development of indices that may not be affected by plant microclimate.
Bremia lactucae is an obligate, oomycete pathogen of lettuce that causes leaf chlorosis and necrosis that adversely impacts marketability. The disease has been managed with a combination of host resistance and fungicide applications with success over the years. Fungicide applications are routinely made under the assumption that inoculum is always present during favorable environmental conditions. This approach often leads to fungicide resistance in B. lactucae populations. Detection and quantification of airborne B. lactucae near lettuce crops provides an estimation of the inoculum load, enabling more judicious timing of fungicide applications. We developed a qPCR-based assay using a target sequence in mitochondrial DNA for specific detection of B. lactucae. Validation using amplicon sequencing of DNA from 83 geographically diverse isolates, representing 14 Bremia spp., confirmed that the primers developed for the TaqMan assay are species-specific and only amplify template from B. lactucae. DNA from a single sporangium could be detected at a Cq value of 32, and Cq values >35 were considered as nonspecific. The coefficient of determination (R2) for regression between sporangial density derived from flow cytometry and Cq values derived from the qPCR was 0.86. The assay was deployed using spore traps in the Salinas Valley, where nearly half the US lettuce is produced. The deployment of this sensitive B. lactucae-specific assay resulted in the detection of the pathogen during the two-week lettuce-free period as well as during the cropping season. These results demonstrate that this assay will be useful for quantifying inoculum load in and around the lettuce fields and facilitating the timing of fungicide applications.
RNA silencing functions as an anti-virus defense strategy in plants, one that plant viruses counter by producing viral suppressors of RNA silencing (VSRs). VSRs have been identified in three members of the genus Crinivirus but they do not all share identical suppression mechanisms. Here, we used Agrobacterium co-infiltration assays to investigate the suppressor activity of proteins encoded by Lettuce chlorosis virus (LCV). Of 7 LCV proteins (1b, P23, HSP70 homolog, P60, CP, CPm and P27) tested for the suppression of silencing of green fluorescent protein (GFP) expression in wild type Nicotiana benthamiana plants, only P23 suppressed the onset of local silencing. Small-interfering (si)RNA accumulation was reduced in leaves co-infiltrated with P23, suggesting that P23 inhibited the accumulation or enhanced the degradation of siRNA. P23 also inhibited the cell-to-cell and systemic movement of RNA silencing in GFP-expressing transgenic N. benthamiana plants. Expression of P23 via agroinfiltration of N. benthamiana leaves induced local necrosis that increased in severity at elevated temperatures, a novelty given that a direct temperature effect on necrosis severity has not been reported for the other crinivirus VSRs. These results further affirm the sophistication of crinivirus VSRs in mediating the evasion of host's antiviral defenses and in symptom modulation.
Quality assessment of lettuce using artificial neural network
- I C Valenzuela
I. C. Valenzuela et al., "Quality assessment of lettuce using artificial
neural network," HNICEM 2017 -9th Int. Conf. Humanoid,
Nanotechnology, Inf. Technol. Commun. Control. Environ. Manag.,
August 2018, pp. 1-5, 2018.
Transcriptomic view of detached lettuce leaves during storage: A crosstalk between wounding, dehydration and senescence
J. Ripoll et al., "Transcriptomic view of detached lettuce leaves
during storage: A crosstalk between wounding, dehydration and
senescence," Postharvest Biol. Technol., vol. 152, no. February, pp.
73-88, 2019.
Plant Leaf Disease Identification System for Android
- K M Chaitra
- F Anjum
- I P Harshitha
- D M Meghana
- M Rachitha
K. M. Chaitra, F. Anjum, I. P. Harshitha, D. M. Meghana, and M. V
Rachitha, "Plant Leaf Disease Identification System for Android,"
Ijercse, vol. 5, no. 6, pp. 48-53, 2018.
Avaliação do potencial de Pistia stratiotes L. (alface d'água) para a bioindicação e fitorremediação de ambientes aquáticos contaminados com arsênio
- F S Farnese
- J A Oliveira
- F S Lima
- G A Leão
- G S Gusman
- L C Silva
F. S. Farnese, J. A. Oliveira, F. S. Lima, G. A. Leão, G. S. Gusman,
and L. C. Silva, "Avaliação do potencial de Pistia stratiotes L.
(alface d'água) para a bioindicação e fitorremediação de ambientes
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