Comparative study of PCA and LDA for rice seeds quality inspection

Contamination of rice seeds affects the crop quality, yield and price. Inspection of rice seeds for purity is a very important step for quality assessment. Promising results have been achieved using hyperspectral imaging (HSI) for classification of rice seeds. However, the relatively high number of spectral features in HSI data continues to pose problems during classification which necessitates the use of techniques like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for dimensionality reduction and feature extraction. This paper presents a comparative study of LDA and PCA as dimensionality reduction techniques for classification of rice seeds using hyperspectral imaging. The results of LDA and PCA on spectral features extracted from hyperspectral images were used for classification using a Random Forest (RF) classifier. Classification results shows that LDA is a superior dimensionality reduction technique to PCA for quality inspection of rice seeds using hyperspectral imaging

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

Quantifying soybean phenotypes using UAV imagery and machine learning, deep learning methods

Crop breeding programs aim to introduce new cultivars to the world with improved traits to solve the food crisis. Food production should need to be twice of current growth rate to feed the increasing number of people by 2050. Soybean is one the major grain in the world and only US contributes around 35 percent of world soybean production. To increase soybean production, breeders still rely on conventional breeding strategy, which is mainly a 'trial and error' process. These constraints limit the expected progress of the crop breeding program. The goal was to quantify the soybean phenotypes of plant lodging and pubescence color using UAV-based imagery and advanced machine learning. Plant lodging and soybean pubescence color are two of the most important phenotypes for soybean breeding programs. Soybean lodging and pubescence color is conventionally evaluated visually by breeders, which is time-consuming and subjective to human errors. The goal of this study was to investigate the potential of unmanned aerial vehicle (UAV)-based imagery and machine learning in the assessment of lodging conditions and deep learning in the assessment pubescence color of soybean breeding lines. A UAV imaging system equipped with an RGB (red-green-blue) camera was used to collect the imagery data of 1,266 four-row plots in a soybean breeding field at the reproductive stage. Soybean lodging scores and pubescence scores were visually assessed by experienced breeders. Lodging scores were grouped into four classes, i.e., non-lodging, moderate lodging, high lodging, and severe lodging. In contrast, pubescence color scores were grouped into three classes, i.e., gray, tawny, and segregation. UAV images were stitched to build orthomosaics, and soybean plots were segmented using a grid method. Twelve image features were extracted from the collected images to assess the lodging scores of each breeding line. Four models, i.e., extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbor (KNN), and artificial neural network (ANN), were evaluated to classify soybean lodging classes. Five data pre-processing methods were used to treat the imbalanced dataset to improve the classification accuracy. Results indicate that the pre-processing method SMOTE-ENN consistently performs well for all four (XGBoost, RF, KNN, and ANN) classifiers, achieving the highest overall accuracy (OA), lowest misclassification, higher F1-score, and higher Kappa coefficient. This suggests that Synthetic Minority Over-sampling-Edited Nearest Neighbor (SMOTE-ENN) may be an excellent pre-processing method for using unbalanced datasets and classification tasks. Furthermore, an overall accuracy of 96 percent was obtained using the SMOTE-ENN dataset and ANN classifier. On the other hand, to classify the soybean pubescence color, seven pre-trained deep learning models, i.e., DenseNet121, DenseNet169, DenseNet201, ResNet50, InceptionResNet-V2, Inception-V3, and EfficientNet were used, and images of each plot were fed into the model. Data was enhanced using two rotational and two scaling factors to increase the datasets. Among the seven pre-trained deep learning models, ResNet50 and DenseNet121 classifiers showed a higher overall accuracy of 88 percent, along with higher precision, recall, and F1-score for all three classes of pubescence color. In conclusion, the developed UAV-based high-throughput phenotyping system can gather image features to estimate soybean crucial phenotypes and classify the phenotypes, which will help the breeders in phenotypic variations in breeding trials. Also, the RGB imagery-based classification could be a cost-effective choice for breeders and associated researchers for plant breeding programs in identifying superior genotypes.Includes bibliographical references

High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms

Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields

Quinoa phenotyping methodologies: An international consensus

Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.Fil: Stanschewski, Clara S.. King Abdullah University of Science and Technology; Arabia SauditaFil: Rey, Elodie. King Abdullah University of Science and Technology; Arabia SauditaFil: Fiene, Gabriele. King Abdullah University of Science and Technology; Arabia SauditaFil: Craine, Evan B.. Washington State University; Estados UnidosFil: Wellman, Gordon. King Abdullah University of Science and Technology; Arabia SauditaFil: Melino, Vanessa J.. King Abdullah University of Science and Technology; Arabia SauditaFil: Patiranage, Dilan S. R.. King Abdullah University of Science and Technology; Arabia SauditaFil: Johansen, Kasper. King Abdullah University of Science and Technology; Arabia SauditaFil: Schmöckel, Sandra M.. King Abdullah University of Science and Technology; Arabia SauditaFil: Bertero, Hector Daniel. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal. Cátedra de Producción Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Oakey, Helena. University of Adelaide; AustraliaFil: Colque Little, Carla. Universidad de Copenhagen; DinamarcaFil: Afzal, Irfan. University of Agriculture; PakistánFil: Raubach, Sebastian. The James Hutton Institute; Reino UnidoFil: Miller, Nathan. University of Wisconsin; Estados UnidosFil: Streich, Jared. Oak Ridge National Laboratory; Estados UnidosFil: Amby, Daniel Buchvaldt. Universidad de Copenhagen; DinamarcaFil: Emrani, Nazgol. Christian-albrechts-universität Zu Kiel; AlemaniaFil: Warmington, Mark. Agriculture And Food; AustraliaFil: Mousa, Magdi A. A.. Assiut University; Arabia Saudita. King Abdullah University of Science and Technology; Arabia SauditaFil: Wu, David. Shanxi Jiaqi Agri-Tech Co.; ChinaFil: Jacobson, Daniel. Oak Ridge National Laboratory; Estados UnidosFil: Andreasen, Christian. Universidad de Copenhagen; DinamarcaFil: Jung, Christian. Christian-albrechts-universität Zu Kiel; AlemaniaFil: Murphy, Kevin. Washington State University; Estados UnidosFil: Bazile, Didier. Savoirs, Environnement, Sociétés; Francia. Universite Paul-valery Montpellier Iii; FranciaFil: Tester, Mark. King Abdullah University of Science and Technology; Arabia Saudit

Quinoa Phenotyping Methodologies: An International Consensus

Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.EEA FamailláFil: Stanschewski, Clara S. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Rey, Elodie. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Fiene, Gabriele. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Craine, Evan B. Washington State University. Department of Crop and Soil Sciences; Estados UnidosFil: Wellman, Gordon. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Melino, Vanessa J. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Patiranage, Dilan S.R. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Patiranage, Dilan S.R. Christian-Albrechts-University of Kiel. Plant Breeding Institute; AlemaniaFil: Johansen, Kasper. King Abdullah University of Science and Technology. Water Desalination and Reuse Center; Arabia SauditaFil: Schmöckel, Sandra M. University of Hohenheim. Institute of Crop Science. Department Physiology of Yield Stability; AlemaniaFil: Erazzu, Luis Ernesto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Famaillá; Argentina.Fil: Tester, Mark. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia Saudit

Quinoa Phenotyping Methodologies: An International Consensus

Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally

Remote Sensing Application in Biomass Crop Production Systems in Oklahoma

This study was conducted to evaluate the combined effects of nitrogen and cropping systems on biomass yield and quality and to describe the spatial variation of biomass yield, soil carbon and nitrogen within a switchgrass field. Field plots at Stillwater and Woodward in Oklahoma consisting of five nitrogen treatments and three cropping systems were used for the nitrogen x cropping system study and an 8 ha switchgrass field at Chickasha, Oklahoma was used to describe the spatial variability at fine (2.5 m sampling distance) and coarse scale (10 m sampling distance). Remote sensing technique was used to monitor biomass yield and quality to better understand N requirement and usage for production. Semivariogram were used to evaluate spatial variability of the soil parameters and biomass yield. The results of this study showed that maximum yield was produced at both locations with less than 84 kg N ha 1 and high biomass sorghum has potential to produce biomass yield > 20 Mg ha 1 under normal conditions in Oklahoma. The study results also showed that perennial grass systems are more reliable sources of biomass yield, especially under adverse climatic conditions of Oklahoma. Final biomass yield of high biomass sorghum could be predicted using both broadband (aerial photograph) and narrowband (GreenSeeker) normalized difference vegetation index (NDVI) from July to close to harvest, while biomass yield in the perennial grass was best predicted during June to July. Comparing simple ratios and best narrowband indices with partial least square regression (PLSR) models suggested that while PLSR calibration models produced significantly lower error and higher r2 for predicting biomass yield and N concentration within a growing season, the simple ratios and best narrowband indices were more stable and reliable when used for prediction across growing seasons. Spatial pattern in switchgrass field was described using both ground and aerial imagery. The NDVI computed from aerial imagery provided good precision at the fine scale in describing the spatial distribution of switchgrass yield. Remote sensing application in biomass production systems can greatly improve prediction models for predicting biomass yield and quality in feedstock materials with use of optimal hyperspectral narrowband.Plant & Soil Scienc