An Evaluation of Unmanned Aircraft Systems\u27 Ability to Assess Stripe Rust in Large Wheat Breeding Nursies

Stripe Rust (Puccinia striiformis f. sp. tritici) is a foliar disease that significantly impacts global wheat production, and resistant cultivars provide the most efficient method of control. High-throughput phenotyping using unmanned aircraft systems (UAS) offers a potentially more efficient method for field-based phenotyping compared to visual assessment. Here we tested the ability of remote sensing to predict stripe rust severity in a diverse population of 594 soft red winter wheat lines, planted in single-rows, and evaluated them by visually rating stripe rust intensity and remotely using the dark green color index (DGCI), normalized difference vegetation index (NDVI) and blue NDVI. Significant relationships (p In a second study, the effect of plot size (single-row, two-row and four-row) on relationship between visual and remote sensing data (DGCI and NDVI) was explored. We evaluated a panel of 13 genotypes preselected to range from 0 to 100% severity, planted in three plot sizes across two measurement days. Significant (

Multi-layer Biosensor for Pre-symptomatic Detection of Puccinia strifformis, the Causal Agent of Yellow Rust

The yellow rust of wheat (caused by Puccinia striiformis f. sp. tritici) is a devastating fungal infection that is responsible for significant wheat yield losses. The main challenge with the detection of this disease is that it can only be visually detected on the leaf surface between 7 and 10 days after infection, and by this point, counter measures such as the use of fungicides are generally less effective. The hypothesis of this study is to develop and use a compact electrochemical-based biosensor for the early detection of P. striiformis, thus enabling fast countermeasures to be taken. The biosensor that was developed consists of three layers. The first layer mimics the wheat leaf surface morphology. The second layer consists of a sucrose/agar mixture that acts as a substrate and contains a wheat-derived terpene volatile organic compound that stimulates the germination and growth of the spores of the yellow rust pathogen P. s. f. sp. tritici. The third layer consists of a nonenzymatic glucose sensor that produces a signal once invertase is produced by P. striiformis, which comes into contact with the second layer, thereby converting sucrose to glucose. The results show the proof that this innovative biosensor can enable the detection of yellow rust spores in 72 h

Breeding Wheat for Powdery Mildew Resistance: Genetic Resources and Methodologies-A Review

Powdery mildew (PM) of wheat caused by Blumeria graminis f. sp. tritici is among the most important wheat diseases, causing significant yield and quality losses in many countries worldwide. Considerable progress has been made in resistance breeding to mitigate powdery mildew. Genetic host resistance employs either race-specific (qualitative) resistance, race-non-specific (quantitative), or a combination of both. Over recent decades, efforts to identify host resistance traits to powdery mildew have led to the discovery of over 240 genes and quantitative trait loci (QTLs) across all 21 wheat chromosomes. Sources of PM resistance in wheat include landraces, synthetic, cultivated, and wild species. The resistance identified in various genetic resources is transferred to the elite genetic background of a well-adapted cultivar with minimum linkage drag using advanced breeding and selection approaches. In this effort, wheat landraces have emerged as an important source of allelic and genetic diversity, which is highly valuable for developing new PM-resistant cultivars. However, most landraces have not been characterized for PM resistance, limiting their use in breeding programs. PM resistance is a polygenic trait; therefore, the degree of such resistance is mostly influenced by environmental conditions. Another challenge in breeding for PM resistance has been the lack of consistent disease pressure in multi-environment trials, which compromises phenotypic selection efficiency. It is therefore imperative to complement conventional breeding technologies with molecular breeding to improve selection efficiency. High-throughput genotyping techniques, based on chip array or sequencing, have increased the capacity to identify the genetic basis of PM resistance. However, developing PM-resistant cultivars is still challenging, and there is a need to harness the potential of new approaches to accelerate breeding progress. The main objective of this review is to describe the status of breeding for powdery mildew resistance, as well as the latest discoveries that offer novel ways to achieve durable PM resistance. Major topics discussed in the review include the genetic basis of PM resistance in wheat, available genetic resources for race-specific and adult-plant resistance to PM, important gene banks, and conventional and complimentary molecular breeding approaches, with an emphasis on marker-assisted selection (MAS)

Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers

Plant Breeding and Management Strategies to Minimize the Impact of Water Scarcity and Biotic Stress in Cereal Crops under Mediterranean Conditions

Wheat and rice are two main staple food crops that may suffer from yield losses due to drought episodes that are increasingly impacted by climate change, in addition to new epidemic outbreaks. Sustainable intensification of production will rely on several strategies, such as efficient use of water and variety improvement. This review updates the latest findings regarding complementary approaches in agronomy, genetics, and phenomics to cope with climate change challenges. The agronomic approach focuses on a case study examining alternative rice water management practices, with their impact on greenhouse gas emissions and biodiversity for ecosystem services. The genetic approach reviews in depth the latest technologies to achieve fungal disease resistance, as well as the use of landraces to increase the genetic diversity of new varieties. The phenomics approach explores recent advances in high-throughput remote sensing technologies useful in detecting both biotic and abiotic stress effects on breeding programs. The complementary nature of all these technologies indicates that only interdisciplinary work will ensure significant steps towards a more sustainable agriculture under future climate change scenarios.info:eu-repo/semantics/publishedVersio

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

Remote sensing and machine learning for prediction of wheat growth in precision agriculture applications

This thesis focuses on remote sensing and machine learning for prediction of wheat growth in precision agriculture applications. Agriculture is the primary productive force, which plays an important role in human activities. Wheat, as one of the essential sources of food, is also a widely planted crop. The impact of weather and climate and some other uncertain factors on wheat production is crucial. Therefore, it is necessary to use reliable and statistically reasonable models for crop growth and yield prediction based on vegetation index variables and other factors, so as to obtain reliable prediction for efficient production. Applying certain artificial intelligence algorithms to the precision agriculture can significantly improve the efficiency of traditional agriculture in crop planting and reduce the consumption of human and natural resources. Remote sensing can objectively, accurately and timely provide a large amount of information for ecological environment and crop growth in agriculture applications. By combining the image and spectral data obtained by remote sensing technology with machine learning, information about wheat growth, yield and insect pests can be learned in time. This thesis focuses on its applications in agriculture, particularly using effective prediction models such as the back propagation neural network and some optimisation algorithms for predicting wheat growth, yield and aphid. The work presented in this thesis address the issues of wheat growth prediction, yield assessment and aphid validation by model building and machine learning algorithm optimisation by means of remote sensing data. Specifically, the following objectives are defined: 1. Analyse multiple vegetation indexes based on the TM 1-4 band data of Landsat satellite and use regression algorithms to train the models and predict wheat growth; 2. Analyse and compare multiple vegetation indexes models by means of spectral data and use regression algorithms to predict wheat yield; 3. Combine spectral vegetation indexes and multiple regression algorithms to predict wheat aphid; 4. Use accurate evaluation criteria for validating the efficacy of the various algorithms. In this thesis, the remote sensing data from the satellite has been applied instead of the airborne-based remote sensing data. Based on the TM 1-4 band image data of Landsat satellite, multiple vegetation indexes were used as the input of regression algorithms. After that, four kinds of regression algorithms such as the multiple linear regression (MR) algorithm, back propagation network (BPNN) algorithm, genetic algorithm (GA) optimised BPNN algorithm and particle swarm optimisation (PSO) optimised BPNN algorithm were used to train the model and predict the LAI and SPAD. The prediction results of each algorithm were compared with the ground truth information collected by hand held instruments on the ground. The relationship between wheat yield and spectral data has been studied. Based on the BPNN algorithm, four kinds of models such as visible hyperspectral index (VHI) model, hyperspectral vegetation index (HVI) model, difference hyperspectral index (DHI) model and normalized hyperspectral index (NHI) model have been utilized to predict wheat yield. For the optimal NHI model, three regression algorithms such as back propagation network (BPNN) algorithm, genetic algorithm (GA) optimised BPNN algorithm and particle swarm optimisation (PSO) optimised BPNN algorithm, were compared to predict wheat yield, and RMSE and R-square of the three algorithms were compared and analysed. Finally, the relationship between wheat aphid and spectral data has been investigated. Nine vegetation indexes related to aphid have been estimated from spectral data as the input of regression algorithms. Five kinds of regression algorithms such as back propagation network (BPNN) algorithm, genetic algorithm (GA) optimised BPNN algorithm, particle swarm optimisation (PSO) optimised BPNN algorithm, ant colony (ACO) optimisation algorithm optimised BPNN algorithm and cuckoo search (CS) optimised BPNN algorithm have been implemented to predict wheat aphid, which was validated with the ground truth information measured by hand-held instruments on the ground. The prediction results of each algorithm have been analysed. The major original contributions of this thesis are as follows: 1. A variety of optimisation algorithms are used to improve the regression analysis of the BPNN algorithm, so that the prediction results of each model for wheat growth, yield and aphid are more accurate. 2. The spectral characteristics of winter wheat canopy have been analysed. The correlation between the absorption band and the associated physical and chemical properties of crops, specially the red edge slope, with the crop yield and wheat aphid damage is established. 3. Adjusted MSE and un-centered R-square, as accurate evaluation criteria for practical applications, are used to compare the prediction results of the models under different dimensions of the observed data. 4. Improve algorithm training by using the cross-validation method to obtain reliable and stable models for the prediction of wheat growth, yield, and aphid. Through repeated cross-validation, a better model can be obtained in the last. Key word：Precision agriculture; BP network, wheat growth assessment; wheat yield prediction, wheat aphid validationThis thesis focuses on remote sensing and machine learning for prediction of wheat growth in precision agriculture applications. Agriculture is the primary productive force, which plays an important role in human activities. Wheat, as one of the essential sources of food, is also a widely planted crop. The impact of weather and climate and some other uncertain factors on wheat production is crucial. Therefore, it is necessary to use reliable and statistically reasonable models for crop growth and yield prediction based on vegetation index variables and other factors, so as to obtain reliable prediction for efficient production. Applying certain artificial intelligence algorithms to the precision agriculture can significantly improve the efficiency of traditional agriculture in crop planting and reduce the consumption of human and natural resources. Remote sensing can objectively, accurately and timely provide a large amount of information for ecological environment and crop growth in agriculture applications. By combining the image and spectral data obtained by remote sensing technology with machine learning, information about wheat growth, yield and insect pests can be learned in time. This thesis focuses on its applications in agriculture, particularly using effective prediction models such as the back propagation neural network and some optimisation algorithms for predicting wheat growth, yield and aphid. The work presented in this thesis address the issues of wheat growth prediction, yield assessment and aphid validation by model building and machine learning algorithm optimisation by means of remote sensing data. Specifically, the following objectives are defined: 1. Analyse multiple vegetation indexes based on the TM 1-4 band data of Landsat satellite and use regression algorithms to train the models and predict wheat growth; 2. Analyse and compare multiple vegetation indexes models by means of spectral data and use regression algorithms to predict wheat yield; 3. Combine spectral vegetation indexes and multiple regression algorithms to predict wheat aphid; 4. Use accurate evaluation criteria for validating the efficacy of the various algorithms. In this thesis, the remote sensing data from the satellite has been applied instead of the airborne-based remote sensing data. Based on the TM 1-4 band image data of Landsat satellite, multiple vegetation indexes were used as the input of regression algorithms. After that, four kinds of regression algorithms such as the multiple linear regression (MR) algorithm, back propagation network (BPNN) algorithm, genetic algorithm (GA) optimised BPNN algorithm and particle swarm optimisation (PSO) optimised BPNN algorithm were used to train the model and predict the LAI and SPAD. The prediction results of each algorithm were compared with the ground truth information collected by hand held instruments on the ground. The relationship between wheat yield and spectral data has been studied. Based on the BPNN algorithm, four kinds of models such as visible hyperspectral index (VHI) model, hyperspectral vegetation index (HVI) model, difference hyperspectral index (DHI) model and normalized hyperspectral index (NHI) model have been utilized to predict wheat yield. For the optimal NHI model, three regression algorithms such as back propagation network (BPNN) algorithm, genetic algorithm (GA) optimised BPNN algorithm and particle swarm optimisation (PSO) optimised BPNN algorithm, were compared to predict wheat yield, and RMSE and R-square of the three algorithms were compared and analysed. Finally, the relationship between wheat aphid and spectral data has been investigated. Nine vegetation indexes related to aphid have been estimated from spectral data as the input of regression algorithms. Five kinds of regression algorithms such as back propagation network (BPNN) algorithm, genetic algorithm (GA) optimised BPNN algorithm, particle swarm optimisation (PSO) optimised BPNN algorithm, ant colony (ACO) optimisation algorithm optimised BPNN algorithm and cuckoo search (CS) optimised BPNN algorithm have been implemented to predict wheat aphid, which was validated with the ground truth information measured by hand-held instruments on the ground. The prediction results of each algorithm have been analysed. The major original contributions of this thesis are as follows: 1. A variety of optimisation algorithms are used to improve the regression analysis of the BPNN algorithm, so that the prediction results of each model for wheat growth, yield and aphid are more accurate. 2. The spectral characteristics of winter wheat canopy have been analysed. The correlation between the absorption band and the associated physical and chemical properties of crops, specially the red edge slope, with the crop yield and wheat aphid damage is established. 3. Adjusted MSE and un-centered R-square, as accurate evaluation criteria for practical applications, are used to compare the prediction results of the models under different dimensions of the observed data. 4. Improve algorithm training by using the cross-validation method to obtain reliable and stable models for the prediction of wheat growth, yield, and aphid. Through repeated cross-validation, a better model can be obtained in the last. Key word：Precision agriculture; BP network, wheat growth assessment; wheat yield prediction, wheat aphid validatio

Remote sensing of biotic stress in crop plants and its applications for pest management

Not AvailableRemote sensing (RS) of biotic stress is based on the assumption that stress interferes with photosynthesis and physical structure of the plant at tissue and canopy level, and thus affects the absorption of light energy and alters the refl ectance spectrum. Research into vegetative spectral refl ectance can help us gain a better understanding of the physical, physiological and chemical processes in plants due to pest and disease attack and to detect the resulting biotic stress. This has important implications to effective pest management. This review provides an overview of detection of various biotic stresses in different crops using various RS platforms. Previous work pertaining to the use of RS technique for assessing pest and disease severity using different RS techniques is briefl y summerized. The available sources of ground based, airborne and satellite sensors are presented along with various narrow band vegetation indices that could be used for characterizing biotic stress. Using relevant examples, the merits and demerits of various RS sensors and platforms for detection of pests and diseases are discussed. Pest surveillance programs such as fi eld scoutings are often expensive, time consuming, laborious and prone to error. As remote sensing gives a synoptic view of the area in a non-destructive and noninvasive way, this technology could be effective and provide timely information on spatial variability of pest damage over a large area. Thus remote sensing can guide scouting efforts and crop protection advisory in a more precise and effective manner. With the recent advancements in the communication, aviation and space technology, there is a lot of potential for application of remote sensing technology in the fi eld of pest management.Not Availabl