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Usability of Foldscope in Food Quality Assessment Device
Abstract
This work focuses on the quality assessment of agricultural product based on microscopic image, generated by Foldscope. Microscopic image-based food quality assessment always be an efficient method, but its system complexity, costly, bulk size and requirement of special expertise confines it usability. To encounter such issues, Foldscope which is small, lightweight, cheap and easy to use has been considered to verify its usability as food quality assessment device. In this purpose, measuring starch of potato has been selected to check its compatibility and microscopic images are taken from two image modalities—conventional microscope and Foldscope and the results have been compared. The image processing techniques including morphological filtering followed by Otsu’s method has been employed to detect starch efficiently. In total, 20 images from each of the system have been captured. Following the experiment, the presence of starch (in %) estimated based on the image taken from microscope and Foldscope are 23.50 ± 0.79 and 24.29 ± 0.73 respectively, which is consistent. Such results reveal that the Foldscope can be used in food quality assessment system, which could make such devices simple, portable and handy.
This work presents a large-scale three-fold annotated, low-cost microscopy image dataset of potato tubers for plant cell analysis in deep learning (DL) framework which has huge potential in the advancement of plant cell biology research. Indeed, low-cost microscopes coupled with new generation smartphones could open new aspects in DL-based microscopy image analysis, which offers several benefits including portability, easy to use, and maintenance. However, its successful implications demand properly annotated large number of diverse microscopy images, which has not been addressed properly- that confines the advanced image processing based plant cell research. Therefore, in this work, a low-cost microscopy image database of potato tuber cells having total 34,657 number of images, has been generated by Foldscope (costs around 1 USD) coupled with a smartphone. This dataset includes 13,369 unstained and 21,288 stained (safranin-o, toluidine blue-o, and lugol's iodine) images with three-fold annotation based on weight, section areas, and tissue zones of the tubers. The physical image quality (e.g., contrast, focus, geometrical attributes, etc.) and its applicability in the DL framework (CNN-based multi-class and multi-label classification) have been examined and results are compared with the traditional microscope image set. The results show that the dataset is highly compatible for the DL framework.
Apple maturity Physicochemical parameters Colour image Multivariate analysis Mexican apple production suffers high losses due to poor handling in processing. Implementation of a straightforward, low cost method to sort apples by their ripening stage is required. A set of Golden Delicious apples was used to monitor their physicochemical properties and external colour, a second set of apples was used to validate the method. To classify the stages, a ripening index (RPI) was proposed, in which three stages were identified ; unripe, ripe and senescent. Weibull model was applied to the physicochemical parameters in order to describe their kinetic behaviour. The three RPI stages were compared with colour variability using the CIELab colour space, chroma (C *) and hue angle (h *), allowing the identification of the three ripening stages. Principal component analysis was used to evaluate the correlation between variables. A first correlation was performed between physicochemical and colour parameters and variables correlated correctly between each other except for L * , but both described the samples variability with 91.05% reliability. Using only colour parameters, the samples were described accurately with 95.06% reliability. Multivariate discriminant analysis (MDA) was done in order to validate the method. A cross-validation was performed with an initial set of apples used as trial samples and a second set of apples for validation. MDA was capable of classifying apples in their correct ripening stage with 100% accuracy. A second analysis was carried out using four colour parameters (a * , b * , C and h *), and results indicated that the ripening stages can be classified with 100% accuracy.
This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0.807 to 0.838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.
Agriculture sector plays a key role in the economic development of India. The task of fruit grading is vital in the agricultural industry because there is a great demand for high quality fruits in the market. However, fruit grading by human is inefficient, labor intensive and prone to error. The automated grading system not only speeds up the time of processing, but also minimizes error. There is a great demand for tomatoes in both local and foreign markets. The tomato fruit is very delicate and hence careful handling of this fruit is required during grading. Thus, this paper proposes an automatic and effective tomato fruit grading system based on computer vision techniques. The proposed quality evaluation method consists of two phases: development of hardware and software. The hardware is developed to capture the image of the tomato and move the fruit to the appropriate bins without manual intervention. The software is developed using image processing techniques to analyze the fruit for defects and ripeness. Experiments were carried out on several images of the tomato fruit. It was observed that the proposed method was successful with 96.47% accuracy in evaluating the quality of the tomato.
Morphological operations are simple to use and works on the basis of set theory. The objective of using morphological operations is to remove the imperfections in the structure of image. Most of the operations used here are combination of two processes, dilation and erosion. The operation uses a small matrix structure called as structuring element. The shape and size of the structuring element has significant impact on the final result. In this paper an attempt is made to understand the basic morphological operations by using them on some standard images. Matlab software is used as the tool for experimenting the morphological operations.
Papaya grading is performed manually which may lead to misclassifications, resulting in fruit boxes with different maturity stages. The objective is to predict the ripening of the papaya fruit using digital imaging and random forests. A series of physical/chemical analyses are carried out and true maturity stage is derived from pulp firmness measurements. Imaging and image analysis provides hand-crafted color features computed from the peel and random decision forests are implemented to predict ripening stage. More specifically, a total of 114 samples from 57 fruits are used for the experiments, and classified into three stages of maturity. After image acquisition and analysis, twenty-one hand-crafted color features (comprising seven groups) that have low computational cost are extracted and evaluated. Random forests with two datasets (cross-validation and prediction set) are employed for the experiments. Concerning all image features, 94.3% classification performance is obtained over the cross-validation set. The prediction set obtained 94.7% misclassifying only a single sample. For the group comparisons, the normalized mean of the RGB (red, green, blue) color space achieved better performance (78.1%). Essentially, the technique can mature into an industrial application with the right integration framework.
Potato is one of the most important and widely cultivated vegetable crops of Assam and ranks fourth in terms of acreage under individual crop in the state. A study on Production behaviour of potato across size groups, districts and zones of Assam was conducted using data pertaining to Agricultural year 2013–14. The result of the study revealed that per hectare potato cultivation cost was found to be highest in LBVZ (Lower Brahmaputra Valley Zone) 70362.08 followed by CBVZ (Central Brahmaputra Valley Zone) 70357.07, UBVZ (Upper Brahmaputra Valley Zone) 70238.78 and NBPZ (North Bank Plain Zone) of Assam 69755.20 respectively. It was also observed that with increase in size group of farms area allocation under the crop also increased as a result cost of production of potato also recorded an increasing trend in all the zones of the state. This trend of increased area allocation across size groups the degree of commercialization of potato had also increased and productivity of the crop was found increasing gradually from size group I farmers to size group III farmers. Potato production in all the zones reflected that in LBVZ of Assam, from 1987 to 1997 witnessed sharp decrease in potato production. After 1994 till 2004 potato production was moving in upward direction due to incorporation of latest production technologies in the farmers field. Potato production in the CBVZ of Assam was found to be more or less unchanged during the whole period. In UBVZ it was reflected that from 1993 till 2003 potato production was found to be stagnant. After that it was showing upward movement. Similarly, in NBPZ also the potato production was found to be more or less stationary during the whole period.
The aim of the present study was to estimate the quality deterioration of apple fillings during storage. Moreover, a potentiality of novel time-saving and non-invasive method based on fluorescence microscopy for prompt ascertainment of non-enzymatic browning initiation in fruit fillings was investigated. Apple filling samples were obtained by mixing different quantities of fruit and stabilizing agents (inulin, pectin and gellan gum), thermally processed and stored for 6-month. The preservation of antioxidant capacity (determined by DPPHradical dot method) in apple fillings was indirectly correlated with decrease in total polyphenols content that varied from 34 ± 22 to 56 ± 17% and concomitant accumulation of 5-hydroxymethylfurfural (HMF), ranging from 3.4 ± 0.1 to 8 ± 1 mg/kg in comparison to initial apple puree values. The mean intensity of the fluorescence emission spectra of apple filling samples and initial apple puree was highly correlated (R2 > 0.95) with the HMF content, showing a good potentiality of fluorescence microscopy method to estimate non-enzymatic browning.
The potato is an important vegetable and can be consumed in different forms, either fresh as table stock or processed into various products. The quality of potato tubers for table and processing use depends on genetic and environmental factors, and storage temperature, duration, and subsequent conditioning. Starch is the major carbohydrate in potato and is an agriculturally important commodity with many food and nonfood uses. It is the basic source of energy for a majority of the world's population. Since starch is the major component of the dry matter of potato, its molecular organization and interactions with nonstarch polysaccharides and sugars are important factors influencing sensory attributes and shelf life of potato products, such as mashed potatoes, French fries, and potato chips. Starch plays a major part in supplying the metabolic energy that enables the body to perform its different functions. Starch is classified into rapidly digestible starch (RDS), slowly digestible starch (SDS), and resistant starch (RS), according to the rate of glucose release and its absorption in the gastrointestinal tract.
Microstructural information about fresh fruit is difficult to obtain using standard electron microscopy methods. Frozen fruit presents additional temperature-related challenges. However, the food industry would benefit from data that show structure/function relationships in fruit and changes that occur during freezing and frozen storage.
The feasibility of using cold stage scanning electron microscopy (cryo-SEM) to describe and define the structure/function relationships of quality attributes of frozen wild blueberries was tested. Cryo-SEM was used to evaluate the structure of wild blueberries that had been frozen using three different methods, stored at three different temperatures, and held in frozen storage for 5 months. Micro-structural data were then compared with quality measures, which included changes in percentage drip, damaged berries, and anthocyanin leakage, to explore relationships between structural changes and observed quality changes.
Cryo-SEM results indicated that microstructural changes observed in frozen blueberry samples are characteristic of their temperature history. Quality measurements and microstructural data are in agreement that improvement of frozen blueberry quality resulted from faster freezing rates and lower temperatures of storage than are used presently in standard production methods. This study indicates that cryo-SEM can be a valuable tool in assessing frozen blueberry quality and developing optimal conditions for freezing wild blueberries.
The United Nations General Assembly decided in December 2005 to declare 2008 as the International Year of the Potato (IYP). Potato is the world's number one non-grain food commodity, with production reaching a record of 320 million tonnes in 2007 (FAO, 2007). Consumption is expanding steadily in the developing countries, where potato is an increasingly important source of food, employment and income. Global interest in the potato increased sharply in 2008 as world food prices soared, threatening the food security and stability of dozens of low-income countries. Unlike major cereals, the potato is not a globally traded commodity and prices are usually determined by local production costs. Increasingly, therefore, the potato is being seen as a vital food-security crop and as a substitute for costly cereal imports.
Microscopic image segmentation of Chinese herbal medicine based on region growing algorithm
- Q Liu
- L J Zhang
- X P Liu
- Qing Liu