A study on non-destructive method for detecting Toxin in pepper using Neural networks

Mycotoxin contamination in certain agricultural systems have been a serious concern for human and animal health. Mycotoxins are toxic substances produced mostly as secondary metabolites by fungi that grow on seeds and feed in the field, or in storage. The food-borne Mycotoxins likely to be of greatest significance for human health in tropical developing countries are Aflatoxins and Fumonisins. Chili pepper is also prone to Aflatoxin contamination during harvesting, production and storage periods.Various methods used for detection of Mycotoxins give accurate results, but they are slow, expensive and destructive. Destructive method is testing a material that degrades the sample under investigation. Whereas, non-destructive testing will, after testing, allow the part to be used for its intended purpose. Ultrasonic methods, Multispectral image processing methods, Terahertz methods, X-ray and Thermography have been very popular in nondestructive testing and characterization of materials and health monitoring. Image processing methods are used to improve the visual quality of the pictures and to extract useful information from them. In this proposed work, the chili pepper samples will be collected, and the X-ray, multispectral images of the samples will be processed using image processing methods. The term "Computational Intelligence" referred as simulation of human intelligence on computers. It is also called as "Artificial Intelligence" (AI) approach. The techniques used in AI approach are Neural network, Fuzzy logic and evolutionary computation. Finally, the computational intelligence method will be used in addition to image processing to provide best, high performance and accurate results for detecting the Mycotoxin level in the samples collected.Comment: 11 pages,1 figure; International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, No.4, July 201

Classification system for rain fed wheat grain cultivars using artificial neural network

Artificial neural network (ANN) models have found wide applications, including prediction, classification, system modeling and image processing. Image analysis based on texture, morphology and color features of grains is essential for various applications as wheat grain industry and cultivation. In order to classify the rain fed wheat cultivars using artificial neural network with different neurons number of hidden layers, this study was done in Islamic Azad University, Shahr-e-Rey Branch, during 2010 on 6 main rain fed wheat cultivars grown in different environments of Iran. Firstly, data on 6 colors, 11 morphological features and 4 shape factors were extracted, then these candidated features fed Multilayer Perceptron (MLP) neural network. The topological structure of this MLP model consisted of 21 neurons in the input layer, 6 neurons (Sardari, Sardari 39, Zardak, Azar 2, ABR1 and Ohadi) in the output layer and two hidden layers with different neurons number (21-30-10-6, 21-30-20-6 and 21-30-30-6). Finally, accuracy average for classification of rain fed wheat grains cultivars computed 86.48% and after feature selection application with UTA algorithm increased to 87.22% in 21-30-20-6 structure. The results indicate that the combination of ANN, image analysis and the optimum model architecture 21-30-20-6 had excellent potential for cultivars classification.Key words: Rain fed wheat, grain, artificial neural networks (ANNs),  multilayer perceptron (MLP), feature selection

Object classification using X-ray images

The main aim of the presented research was to assess the possibility of utilizing geometric features in object classification. Studies were conducted using X-ray images of kernels belonging to three different wheat varieties: Kama, Canadian and Rosa. As a part of the work, image processing methods were used to determine the main geometric grain parameters, including the kernel area, kernel perimeter, kernel length and kernel width. The results indicate significant differences between wheat varieties, and demonstrates the importance of their size and shape parameters in the classification process. The percentage of correctness of classification was about 92% when the k-Means algorithm was used. A classification rate of 93% was obtain using the K-Nearest Neighbour and Support Vector Machines. Herein, the Rosa variety was better recognized, whilst the Canadian and Kama varieties were less successfully differentiated

Emerging thermal imaging techniques for seed quality evaluation: Principles and applications

Due to the massive progress occurred in the past few decades in imaging, electronics and computer science, infrared thermal imaging technique has witnessed numerous technological advancement and smart applications in non-destructive testing and quality monitoring of different agro-food produces. Thermal imaging offers a potential non-contact imaging modality for the determination of various quality traits based on the infrared radiation emitted from target foods. The technique has been moved from just an exploration method in engineering and astronomy into an effective tool in many fields for forming unambiguous images called thermograms eventuated from the temperature and thermal properties of the target objects. It depends principally on converting the invisible infrared radiation emitted by the objects into visible two-dimensional temperature data without making a direct contact with the examined objects. This method has been widely used for different applications in agriculture and food science and technology with special applications in seed quality assessment. This article provides an overview of thermal imaging theory, briefly describes the fundamentals of the system and explores the recent advances and research works conducted in quality evaluation of different sorts of seeds. The article comprehensively reviewed research efforts of using thermal imaging systems in seed applications including estimation of seed viability, detection of fungal growth and insect infections, detection of seed damage and impurities, seed classification and variety identification.info:eu-repo/semantics/acceptedVersio

Photosynthesis in non‐foliar tissues: implications for yield

Photosynthesis is currently a focus for crop improvement, however the majority of this work has taken place and been assessed in leaves, whilst limited consideration has been given to the contribution that other green tissues make to whole plant carbon assimilation. The major focus of this review is to evaluate the impact of non‐foliar photosynthesis on carbon use efficiency and total assimilation. Here we appraise and summarise past and current literature on the substantial contribution of different photosynthetically active organs and tissues to productivity in a variety of different plant types, with an emphasis on fruit and cereal crops. Previous studies provide evidence that non‐leaf photosynthesis could be an unexploited potential target for crop improvement. We also briefly examine the role of stomata in non‐foliar tissues and their role in gas exchange, maintenance of optimal temperatures and thus photosynthesis. In the final section, we discuss possible opportunities to manipulate these processes and provide evidence that wheat plants genetically manipulated to increase leaf photosynthesis, also displayed higher rates of ear assimilation, which translated to increased grain yield. By understanding these processes, we can start to provide insights into manipulating non‐foliar photosynthesis and stomatal behaviour to identify novel targets for exploitation for on‐going breeding programmes

Guide to Drought Tolerance of Utah Field Crops

Crop variety selection is one of the most important choices on the farm. Crop genetics determine a significant portion of the yield potential and resource use efficiency. Crop types and genetics that use water more efficiently will become increasingly important as water becomes scarcer. Throughout Utah and the Western United States, water availability is decreasing due to various factors, including reduced snowpack and rapid urban growth. Alfalfa, other hay, small grains, and corn are grown on more acres than any other crops in Utah and much of the Intermountain West. These crops all have varieties, hybrids, and cultivars with the potential for more efficient water use while mitigating yield loss. Navigating these options and understanding various mechanisms and effectiveness can be a challenge. This guide will address some of the primary mechanisms, options, and effectiveness of crop genetics for improved water use efficiency

Remote sensing techniques and stable isotopes as phenotyping tools to assess wheat yield performance: effects of growing temperature and vernalization

This study compares distinct phenotypic approaches to assess wheat performance under different growing temperatures and vernalization needs. A set of 38 (winter and facultative) wheat cultivars were planted in Valladolid (Spain) under irrigation and two contrasting planting dates: normal (late autumn), and late (late winter). The late plating trial exhibited a 1.5 °C increase in average crop temperature. Measurements with different remote sensing techniques were performed at heading and grain filling, as well as carbon isotope composition (δ13C) and nitrogen content analysis. Multispectral and RGB vegetation indices and canopy temperature related better to grain yield (GY) across the whole set of genotypes in the normal compared with the late planting, with indices (such as the RGB indices Hue, a* and the spectral indices NDVI, EVI and CCI) measured at grain filling performing the best. Aerially assessed remote sensing indices only performed better than ground-acquired ones at heading. Nitrogen content and δ13C correlated with GY at both planting dates. Correlations within winter and facultative genotypes were much weaker, particularly in the facultative subset. For both planting dates, the best GY prediction models were achieved when combining remote sensing indices with δ13C and nitrogen of mature grains. Implications for phenotyping in the context of increasing temperatures are further discussed

Feasibility of Impact-Acoustic Emissions for Detection of Damaged Wheat Kernels

Cataloged from PDF version of article.A non-destructive, real time device was developed to detect insect damage, sprout damage, and scab damage in kernels of wheat. Kernels are impacted onto a steel plate and the resulting acoustic signal analyzed to detect damage. The acoustic signal was processed using four different methods: modeling of the signal in the time-domain, computing time-domain signal variances and maximums in short-time windows, analysis of the frequency spectrum magnitudes, and analysis of a derivative spectrum. Features were used as inputs to a stepwise discriminant analysis routine, which selected a small subset of features for accurate classification using a neural network. For a network presented with only insect damaged kernels (IDK) with exit holes and undamaged kernels, 87% of the former and 98% of the latter were correctly classified. It was also possible to distinguish undamaged, IDK, sprout-damaged, and scab-damaged kernels. © 2005 Elsevier Inc. All rights reserved

Hyperspectral imagery combined with machine learning to differentiate genetically modified (GM) and non-GM canola

Canola, also known as rapeseed (Brassica napus L.), is an oilseed that produces a healthy food-grade oil, canola meal by-product, and biofuel. It is the fourth most grown grain in Australia. Genetically modified (GM) canola currently represents approximately twenty percent of national canola production; hence, with clashing public and industry perceptions of genetically modified organisms (GMOs), transparency and traceability must be enabled throughout the supply chain to protect markets and relationships with consumers. GM canola must not cross-contaminate non-GM canola as our largest export market, Europe, has extremely strict protocols on GMOs. GM and non-GM canola cannot be differentiated by the human eye, with polymerase chain reaction (PCR) methods currently the main alternative, which is expensive and time-consuming. This thesis evaluates the potential to differentiate GM from non-GM canola using the novel, rapid, and non-destructive technique of hyperspectral imaging combined with machine learning. Hyperspectral imagery captures and processes wavelengths beyond simply red, green, and blue. It has a pre-existing multitude of uses including the characterisation and variety identification of other grains. In this study 500 images each of non-GM and GM canola seeds were captured. Seeds were placed on a black background with two lights sources. Images were captured from the 400nm to 1000nm wavelengths, a total of 80 bands, at a 25-millisecond exposure time. These images were run through a convolutional neural network in Keras for analysis. The high dynamic range and raw files were combined into a NumPy file for the hyperspectral image generator. Contrary to expectations, however, the models using the bitmap image files performed similarly to the models receiving the hyperspectral images. Regardless, both produced high validation accuracies around 90%, indicating a detectable phenotypical difference between the two, and further studies could lead to the development of a new approach to GM canola detection

The effect of non-glaucousness, as conferred by Inhibitor of Wax 1, on physiology and yield of UK Wheat

Abstract As the first barrier to the external environment, the epicuticular waxes have a number of key roles in plant physiology. Although the wheat wild progenitors display a diversity of epicuticular wax phenotypes, the glaucous (visible wax) phenotype dominates cultivated varieties. However, the UK winter wheat variety Shamrock is unusual in that it exhibits a non-glaucous phenotype, conferred by the wild emmer gene Inhibitor of Wax 1 (Iw1). UK field trials with Shamrock associated a yield advantage of 4.15% with Iw1. This PhD tests the hypothesis that Iw1 imparts an advantage for wheat yield and physiology in the UK. Crossing Shamrock with six glaucous UK winter wheat varieties (Malacca, Alchemy, Hereward, Xi19, Robigus and Einstein) created non-glaucous near isogenic lines (NILs) with Iw1. NILs were grown at multiple field trial locations in the east of England over four years. A long-term shade trial reducing incoming light by 40 and 60% was also carried out in 2014. Yield, and various physiological components including water use efficiency (WUE) and spectral properties, were measured. Iw1 reduced flag leaf photosynthetically active radiation (PAR) reflectance by 15-40% and canopy reflectance by 12-20% (p<0.05). Despite this, Iw1 did not affect flag leaf PAR absorbance or canopy temperature, and conferred no advantage under long-term shading. Furthermore, there was no difference between NILs in photoinhibition following an extended period of high light stress. Iw1 did not affect WUE or yield. However, non-glaucous Hereward and Alchemy NILs yielded 4.96±1.15% (p<0.001) and 2.59±1.01% (p=0.045) more than their glaucous counterparts, although this advantage did not map to Iw1. Iw1 offered no advantage to UK winter wheat under normal UK growing conditions, nor under long-term shading. However, the yield advantage associated with the Iw1 introgression in Hereward and Alchemy is significant within a backdrop of plateauing wheat yields and worth pursuing