HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest

HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest

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

Automatic Assessment of Seed Germination Percentage

This research was designed to investigate an automatic seed germination rate for the top of paper germination method. Chili and guinea were adopted to be used in the experiment with a 4-time repetition and 2 sets of the germination group (4-separated plates with 50 seeds per plate, 2 sets per seed type, totally 400 seeds of chili and 400 seeds of quinea). Two detection methods were proposed binary thresholding and maximum likelihood; based on color analysis. An uncontrolled environment image taking was the way to collect image data. The results were compared to a hand-labeling groundtruth. Both methods achieved accuracy rate higher than 93% which was promising to implement this system. The binary thresholding was a lightweight method suitable for a very limited resource software environment system. The maximum likelihood was more complex. The method had more potential than the binary thresholding, it was flexible to the light condition, returned few false alarms per image (less than 3 false alarms per image). Maximum likelihood could be adopted to implement in a proper environment which still could be in a mobile device

Sensors Application in Agriculture

Novel technologies are playing an important role in the development of crop and livestock farming and have the potential to be the key drivers of sustainable intensification of agricultural systems. In particular, new sensors are now available with reduced dimensions, reduced costs, and increased performances, which can be implemented and integrated in production systems, providing more data and eventually an increase in information. It is of great importance to support the digital transformation, precision agriculture, and smart farming, and to eventually allow a revolution in the way food is produced. In order to exploit these results, authoritative studies from the research world are still needed to support the development and implementation of new solutions and best practices. This Special Issue is aimed at bringing together recent developments related to novel sensors and their proved or potential applications in agriculture

Optimización de la prueba de tetrazolio para evaluar la viabilidad en semillas de Solanum lycopersicum L.

Tomato (Solanum lycopersicum) is one of the most consumed vegetables worldwide with increasing demand. Therefore, knowing its seeds viability is of utmost importance since these are the basis of their production success. Accordingly, the study aimed at optimizing the tetrazolium test by determining the appropriate pretreatment to enhance it. Plant material was collected from crops established in the municipality of Cáchira, department of Norte de Santander, Colombia. The seeds were extracted from ripe fruits and subsequently exposed to preconditioning with sodium hypochlorite and distilled water for 10 minutes, with three concentrations of 2,3,5-triphenyl tetrazolium chloride (0.25 %, 0.15 %, and 0.10 %) and different exposure times (6 h, 12 h, and 24 h). The viability data obtained were corroborated by the germination test on wet paper towels. The viability results with the highest correlation with the germination test were obtained using the 0.25 % and 0.15 % concentrations, and utilizing the pre-conditioning treatment with sodium hypochlorite as well as immersion in distilled water.El tomate (Solanum lycopersicum) es una de las hortalizas más consumidas en el mundo con un aumento en su demanda, por lo que conocer la viabilidad de sus semillas es de suma importancia, ya que estas son la base del éxito de su producción. Debido a esto, el presente estudio tuvo como objetivo optimizar la prueba de tetrazolio determinando el pretratamiento adecuado para potenciar la prueba. El material vegetal se recolectó de cultivos establecidos en el municipio del Cáchira, departamento de Norte de Santander, Colombia. Las semillas se extrajeron de los frutos maduros y, posteriormente, se expusieron a pretratramientos con hipoclorito de sodio y agua destilada durante 10 minutos, con tres concentraciones de 2, 3, 5- cloruro trifenil tetrazolio (0,25 %, 0,15 % y 0,10 %) y distintos tiempos de exposición (6 h, 12 h y 24 h). Los datos de viabilidad obtenidos se corroboraron mediante la prueba de germinación en toallas de papel húmedas. Los resultados de viabilidad más relacionados con la prueba de germinación se obtuvieron al emplear las concentraciones de 0,25 % y 0,15 %, utilizando tanto el pretratamiento con hipoclorito de sodio como la inmersión en agua destilada. &nbsp

Multivariate analysis for quality control of agrifood materials using near infrared spectroscopy

Seguridad y calidad alimentaria son uno de los conceptos más demandados actualmente en la industria agroalimentaria. La mayoría de análisis de control de los productos alimentarios se lleva a cabo mediante métodos tradicionales (vía húmeda). Los principales problemas relacionados con este tipo de análisis son el consumo de tiempo para la obtención de los resultados de una sola muestra, el coste del análisis, así como la limitación en cuanto a su implantación en la línea de producción o en el campo, entre otros. Paralelamente al desarrollo e innovación tecnológica, numerosos métodos han sido implementados para la determinación, evaluación y control de la calidad de los productos agroalimentarios en las últimas décadas. Estos métodos están basados en la detección de varias propiedades tanto físicas como químicas correlacionadas con ciertos factores cualitativos de los productos. Uno de los métodos más difundido y aún en desarrollo debido a su gran aplicabilidad, es la espectroscopía de infrarrojo cercano (tecnología NIRS, Near Infrared Spectroscopy). Han pasado más de 20 años desde su primera introducción como potente herramienta hecha por Karl Norris en el análisis de la composición de los cereales. El planteamiento de esta tesis nace de la necesidad, cada vez mayor, del control de los parámetros de calidad de los productos agroalimentarios de manera rápida y precisa. La categorización del trigo en función de su calidad o el valor añadido que adquiere la soja según el porcentaje de proteína o grasa presente en una determinada variedad ha llevado al estudio de la aplicación de la espectroscopía de infrarrojo cercano en dichos productos. El objetivo general de la investigación ha consistido en la aplicación de la tecnología NIRS para la determinación de parámetros de calidad en muestras de...Food safety and quality are currently the most popular concepts in the food industry. Usually, most control analyses of food products are carried out by conventional methods (wet chemistry). However, some of the main negative issues of these methods are: they are time consuming in order to obtain the results of a single sample, the raising price and the limitation on its implementation in the production line or in the field, among others. At the same time to the technological innovation and development, during the last decades many methods have been implemented for the identification, assessment and quality control of food products. These methods are based on the detection of various physical and chemical properties correlated with certain product quality factors. One of the most widespread due to its wide applicability is the near-infrared spectroscopy (NIRS technology, Near Infrared Spectroscopy). It has been over 20 years since its first introduction as a powerful tool made by Karl Norris in the analysis of the composition of the grains. The approach of this thesis arises from the increasing need of fast and accurate analyses of quality parameters control on food products. The categorization of wheat in terms of quality and the added value acquired by the percentage of soy protein or fat in a particular variety has led to the study of the application of near infrared spectroscopy in these products. The general objective of the research has been the application of NIRS technology for the determination of quality parameters in wheat and soybean samples. As a result, this study has led to the development of four chapters: - "Development of robust soybean NIR Calibration Models with temperature compensation and high variability in the data basis." This chapter was focused on the development of robust calibrations by adding in the group of samples instrumental and environmental variability..

Study on comparison of biochemistry between Trogoderma granarium Everts and Trogoderma variabile Ballion

Stored grains are paramount commodities to be preserved and stocked for future supply to the market according to the requirement. However, one of the major problems during storage is insect pests, of which insects from Trogoderma sp. especially khapra beetle (Trogoderma granarium) is considered the world most dangerous stored grain insect pests. Therefore, it has been listed as quarantine insect pests in many counties. For timely management of quarantine pest, effective and rapid diagnostic methods are required. Until now, diagnostic technology is mainly based on morphology of insects which require trained taxonomists. Recently, diagnostics based on metabolites and hyperspectral imaging coupled with machine learning is gaining importance. However, very little is known about the metabolites in Trogoderma sp. and how the host grain, gender, and geographical distribution affect the metabolomic profiling in these species is still unknown. In this thesis, volatile organic compounds (VOCs) emitted by Trogoderma variabile at different life stages were analysed as biomarkers which can help us to understand the biochemistry and metabolomic. Some compounds were identified from T. variabile different stages, which could be used as diagnostic tool for this insect. Gas chromatography coupled to mass spectrometry (GC–MS) was used as a technique to study the metabolite profile of T. variabile in different host grains. However, there are several factors that affect the volatile organic compounds including extraction time and number of insects. The results indicated that the optimal number of insects required for volatile organic compounds (VOC) extraction at each life stage was 25 and 20 for larvae and adults respectively. Sixteen hours were selected as the optimal extraction time for larvae and adults. Some of the VOCs compounds identified from this insect can be used as biomarkers such as pentanoic acid; diethoxymethyl acetate; 1-decyne; naphthalene, 2-methyl-; n-decanoic acid; dodecane, 1-iodo- and m-camphorene from larvae. While butanoic acid, 2-methyl-; pentanoic acid; heptane, 1,1'-oxybis- 2(3H)-Furanone, 5-ethyldihydro-; pentadecane, 2,6,10-trimethyl-; and 1,14-tetradecanediol VOCs, were found in male, whereas pentadecane; nonanic acid; pentadecane, 2,6,10-trimethyl-; undecanal and hexadecanal were identified from female. Additionaly, direct immersion-solid phase microextraction (DI-SPME) was employed, followed by gas chromatography mass spectrometry analysis (GC-MS) for the collection, separation, and identification of the chemical compounds from T. variabile adults fed on four different host grains. Results showed that insect host grains have a significant difference on the chemical compounds that were identified from female and male. There were 23 compounds identified from adults reared on canola and wheat. However, there were 26 and 28 compounds detected from adults reared on oats and barley respectively. Results showed that 11-methylpentacosane; 13-methylheptacosane; heptacosane; docosane, 1-iodo- and nonacosane were the most significant compounds that identified form T. variabile male reared on different host grains. However, the main compounds identified from female cultured on different host grains include docosane, 1-iodo-; 1-butanamine, N-butyl-; oleic acid; heptacosane; 13-methylheptacosane; hexacosane; nonacosane; 2-methyloctacosane; n-hexadecanoic acid and docosane. A novel diagnostic tool to identify between T. granarium and T. variabile were developed using visible near infrared hyperspectral imaging and deep learning models including Convolutional Neural Networks (CNN) and Capsule Network. Ventral orientation showed a better accuracy over dorsal orientation of the insects for both larvae and adult stages. This technology offers a new approach and possibility of an effective identification of T. granarium and T. variabile. from its body fragments and larvae skins. The results showed high accuracy to identify between T. granarium and T. variabile. The accuracy was 93.4 and 96.2% for adults and larvae respectively, and the accuracies of 91.6, 91.7 and 90.3% were achieved for larvae skin, adult fragments, larvae fragment respectively

Modern Seed Technology

Satisfying the increasing number of consumer demands for high-quality seeds with enhanced performance is one of the most imperative challenges of modern agriculture. In this view, it is essential to remember that the seed quality of crops does not improve