Spectroscopy Technology: An Innovative Tool for Diagnosis and Monitoring of Wheat Diseases

Diseases are among the most important factors limiting worldwide production of wheat. Accurate detection of diseases is the key to develop effective management strategies for control of these diseases. Spectroscopy-based technology can be a non-destructive, quick, efficient tool to accurately detect and monitor the occurrence and development of crop diseases. There has seen an increased interest in the research and application of spectrum technology for the diagnosis and detection of wheat diseases in recent years. This book chapter provides a brief review on research advances in using spectroscopy techniques to detect wheat diseases, with a focus on the diagnosis and detection of Fusarium head blight, powdery mildew, and stripe rust, three important fungal diseases in wheat worldwide. Disease symptoms and traditional disease detection methods are also included. Both literature and our original research data are presented, with the section of conclusion and prospects at the end of this book chapter

Wheat Yield Assessment Using In-Field Organ-Scale Phenotyping and Deep Learning Methods

Phenwhea

A survey of image-based computational learning techniques for frost detection in plants

Frost damage is one of the major concerns for crop growers as it can impact the growth of the plants and hence, yields. Early detection of frost can help farmers mitigating its impact. In the past, frost detection was a manual or visual process. Image-based techniques are increasingly being used to understand frost development in plants and automatic assessment of damage resulting from frost. This research presents a comprehensive survey of the state-of the-art methods applied to detect and analyse frost stress in plants. We identify three broad computational learning approaches i.e., statistical, traditional machine learning and deep learning, applied to images to detect and analyse frost in plants. We propose a novel taxonomy to classify the existing studies based on several attributes. This taxonomy has been developed to classify the major characteristics of a significant body of published research. In this survey, we profile 80 relevant papers based on the proposed taxonomy. We thoroughly analyse and discuss the techniques used in the various approaches, i.e., data acquisition, data preparation, feature extraction, computational learning, and evaluation. We summarise the current challenges and discuss the opportunities for future research and development in this area including in-field advanced artificial intelligence systems for real-time frost monitoring

Image processing techniques for plant phenotyping using RGB and thermal imagery = Técnicas de procesamiento de imágenes RGB y térmicas como herramienta para fenotipado de cultivos

[eng] World cereal stocks need to increase in order to meet growing demands. Currently, maize, rice, wheat, are the main crops worldwide, while other cereals such as barley, sorghum, oat or different millets are also well placed in the top list. Crop productivity is affected directly by climate change factors such as heat, drought, floods or storms. Researchers agree that global climate change is having a major impact on crop productivity. In that way, several studies have been focused on climate change scenarios and more specifically abiotic stresses in cereals. For instance, in the case of heat stress, high temperatures between anthesis to grain filling can decrease grain yield. In order to deal with the climate change and future environmental scenarios, plant breeding is one of the main alternatives breeding is even considered to contribute to the larger component of yield growth compared to management. Plant breeding programs are focused on identifying genotypes with high yields and quality to act as a parentals and further the best individuals among the segregating population thus develop new varieties of plants. Breeders use the phenotypic data, plant and crop performance, and genetic information to improve the yield by selection (GxE, with G and E indicating genetic and environmental factors). More factors must be taken into account to increase the yield, such as, for instance, the education of farmers, economic incentives and the use of new technologies (GxExM, with M indicating management). Plant phenotyping is related with the observable (or measurable) characteristics of the plant while the crop growing as well as the association between the plant genetic background and its response to the environment (GxE). In traditional phenotyping the measurements are collated manually, which is tedious, time consuming and prone to subjective errors. Nowadays the technology is involved in many applications. From the point of view of plan phenotyping, technology has been incorporated as a tool. The use of image processing techniques integrating sensors and algorithm processes, is therefore, an alternative to asses automatically (or semi-automatically) these traits. Images have become a useful tool for plant phenotyping because most frequently data from the sensors are processed and analyzed as an image in two (2D) or three (3D) dimensions. An image is the arrangement of pixels in a regular Cartesian coordinates as a matrix, each pixel has a numerical value into the matrix which represents the number of photons captured by the sensor within the exposition time. Therefore, an image is the optical representation of the object illuminated by a radiating source. The main characteristics of images can be defined by the sensor spectral and spatial properties, with the spatial properties of the resulting image also heavily dependent on the sensor platform (which determines the distance from the target object).[spa] Las existencias mundiales de cereales deben aumentar para satisfacer la creciente demanda. Actualmente, el maíz, el arroz y el trigo son los principales cultivos a nivel mundial, otros cereales como la cebada, el sorgo y la avena están también bien ubicados en la lista. La productividad de los cultivos se ve afectada directamente por factores del cambio climático como el calor, la sequía, las inundaciones o las tormentas. Los investigadores coinciden en que el cambio climático global está teniendo un gran impacto en la productividad de los cultivos. Es por esto que muchos estudios se han centrado en escenarios de cambio climático y más específicamente en estrés abiótico. Por ejemplo, en el caso de estrés por calor, las altas temperaturas entre antesis y llenado de grano pueden disminuir el rendimiento del grano. Para hacer frente al cambio climático y escenarios ambientales futuros, el mejoramiento de plantas es una de las principales alternativas; incluso se considera que las técnicas de mejoramiento contribuyen en mayor medida al aumento del rendimiento que el manejo del cultivo. Los programas de mejora se centran en identificar genotipos con altos rendimientos y calidad para actuar como progenitores y promover los mejores individuos para desarrollar nuevas variedades de plantas. Los mejoradores utilizan los datos fenotípicos, el desempeño de las plantas y los cultivos, y la información genética para mejorar el rendimiento mediante selección (GxE, donde G y E indican factores genéticos y ambientales). El fenotipado plantas está relacionado con las características observables (o medibles) de la planta mientras crece el cultivo, así como con la asociación entre el fondo genético de la planta y su respuesta al medio ambiente (GxE). En el fenotipado tradicional, las mediciones se clasifican manualmente, lo cual es tedioso, consume mucho tiempo y es propenso a errores subjetivos. Sin embargo, hoy en día la tecnología está involucrada en muchas aplicaciones. Desde el punto de vista del fenotipado de plantas, la tecnología se ha incorporado como una herramienta. El uso de técnicas de procesamiento de imágenes que integran sensores y algoritmos son por lo tanto una alternativa para evaluar automáticamente (o semiautomáticamente) estas características

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

3D Convolutional Neural Networks for Solving Complex Digital Agriculture and Medical Imaging Problems

3D signals have become widely popular in view of the advantage they provide via 3D representations of data by employing a third spatial or temporal dimension to extend 2D signals. Predominantly, 3D signals contain details inexistent in their 2D counterparts such as the depth of an image, which is inherent to point clouds (PC), or the temporal evolution of an image, which is inherent to time series data such as videos. Despite this advantage, 3D models are still underexploited in machine learning (ML) compared to 2D signals, mainly due to data scarcity. In this thesis, we exploit and determine the efficiency and influence of using both multispectral PCs and time-series data with 3D convolutional neural networks (CNNs). We evaluate the performance and utility of these networks and data in the context of two applications from the areas of digital agriculture and medical imaging. In particular, multispectral PCs are investigated for the problem of fusarium-head-blight (FHB) detection and total number of spikelets estimation, while time-series echocardiography are investigated for the problem of myocardial infarction (MI) detection. In the context of the digital agriculture application, two state-of-the-art datasets were created, namely the UW-MRDC WHEAT-PLANT PC dataset, consisting of 216 multispectral PC of wheat plants, and the UW-MRDC WHEAT-HEAD PC dataset, consisting of 80 multispectral PC of wheat heads. Both dataset samples were acquired using a multispectral 3D scanner. Moreover, a real-time parallel GPU-enabled preprocessing method, that runs 1065 times faster than its CPU counterpart, was proposed to convert multispectral PCs into multispectral 3D images compatible with CNNs. Also, the UW-MRDC WHEAT-PLANT PC dataset was used to develop novel and efficient 3D CNNs for disease detection to automatically identify wheat infected with FHB from multispectral 3D images of wheat plants. In addition, the influence of the multispectral information on the detection performance was evaluated, and our results showed the dominance of the red, green, and blue (RGB) colour channels over both the near-infra-red (NIR) channel and RGB and NIR channels combined. Our best model for FHB detection in wheat plants achieved 100% accuracy. Furthermore, the UW-MRDC WHEAT-HEAD PC dataset was used to develop unique and efficient 3D CNNs for total number of spikelets estimation in multispectral 3D images of wheat heads, in addition to adapting three benchmark 2D CNN architectures to 3D images to achieve the same purpose. Our best model for total number of spikelets estimation in wheat head achieved 1.13 mean absolute error, meaning that, on average, the difference between the estimated number of spikelets and the actual value is equal to 1.13. Our 3D CNN for FHB detection in wheat achieved the highest accuracy amongst existing FHB detection models, and our 3D CNN for total number of spikelets estimation in wheat is a unique and pioneer application. These results suggest that replacing arduous tasks that require the input of field experts and significant temporal resources with automated ML models in the context of digital agriculture is feasible and promising. In the context of the medical imaging application, an innovative, real-time, and fully automated pipeline based on 2D and 3D CNNs was proposed for early detection of MI, which is a deadly cardiac disorder, from a patient’s echocardiography. The developed pipeline consists of a 2D CNN that performs data preprocessing by segmenting the left ventricle (LV) chamber from the apical 4-chamber (A4C) view from an echocardiography, followed by a 3D CNN that performs MI detection in real-time. The pipeline was trained and tested on the HMC-QU dataset consisting of 162 echocardiography. The 2D CNN achieved 97.18% accuracy on data segmentation, and the 3D CNN achieved 90.9% accuracy, 100% precision, 95% recall, and 97.2% F1 score. Our detection results outperformed existing state-of-the-art models that were tested on the HMC-QU dataset for MI detection. Moreover, our results demonstrate that developing a fully automated system for LV segmentation and MI detection is efficient and propitious and could enable the creation of a tool that reliably suggests the presence of MI in a given echocardiography on the fly. All the empirical results achieved in our thesis indicate the efficiency and reliability of 3D signals, that are multispectral PCs and videos, in developing detection and regression 3D CNN models that can achieve accurate and reliable results.Mitacs, EMILI, NSERC, Western Diversification Canada, The Faculty of Graduate Studies.Master of Science in Applied Computer Scienc

The effect of low ground pressure and controlled traffic farming systems on soil properties and crop development for three tillage systems

Soil management is an integral part of agricultural systems, yet soil degradation from processes such as erosion, loss of organic matter and compaction, as a result of agriculture, is a worldwide environmental problem that threatens future crop yields. Modern crop production systems require increasingly more powerful and heavier machinery and consequential soil compaction is now a major problem, responsible for soil degradation of an area of 33 million ha in Europe. This research examined the effect of differing soil managementstrategies (three traffic systems: Random Traffic Farming with standard tyre inflation pressure, Random Traffic Farming with low tyre inflation pressure and Controlled Traffic Farming on a sandy loam soil cultivated with three tillage systems: deep (250 mm), shallow (100 mm) and no-till),on crop growth and yield and the corresponding effect on soil physical properties using the innovative technique of X-ray Computed Tomography.There was no significant difference in crop yield between deep and shallow tillage but deep tillage significantly (P=0.030) reduced the soil shear strength, leaving soils prone to compaction by subsequent field traffic. Using shallow rather than deep tillage provides an opportunity to reduce fuel costs associated with the reduction in draft force required for the tillage operations. Zero tillage significantly (P<0.001) reduced crop yields compared to shallow tillage by up to 15%.As part of this study anovel technique was developed, for determining the totalporosity that allowed a comparison of soil porosities derived from bulk density measurements and X-ray CT measured porosities and found that a constant of 31% could be added to the X-ray CT porosities to give the total physical soil porosit