Identification of Foliar Diseases in Cotton Crop

The manifestation of pathogens in plantations is the most important cause of losses in several crops. These usually represent less income to the farmers due to the lower product quality as well as higher prices to the consumer due to the smaller offering of goods. The sooner the disease is identified the sooner one can control it through the use of agrochemicals, avoiding great damages to the plantation. This chapter introduces a method for the automatic classification of cotton diseases based on the feature extraction of foliar symptoms from digital images. The method uses the energy of the wavelet transform for feature extraction and a Support Vector Machine for the actual classification. Five possible diagnostics are provided: 1) healthy (SA), 2) injured with Ramularia disease (RA), 3) infected with Bacterial Blight (MA), 4) infected with Ascochyta Blight (AS), or 5) possibly infected with an unknown disease

Designing AfriCultuReS services to support food security in Africa

ABSTRACT: Earth observation (EO) data are increasingly being used to monitor vegetation and detect plant growth anomalies due to water stress, drought, or pests, as well as to monitor water availability, weather conditions, disaster risks, land use/land cover changes and to evaluate soil degradation. Satellite data are provided regularly by worldwide organizations, covering a wide variety of spatial, temporal and spectral characteristics. In addition, weather, climate and crop growth models provide early estimates of the expected weather and climatic patterns and yield, which can be improved by fusion with EO data. The AfriCultuReS project is capitalizing on the above to contribute towards an integrated agricultural monitoring and early warning system for Africa, supporting decision making in the field of food security. The aim of this article is to present the design of EO services within the project, and how they will support food security in Africa. The services designed cover the users' requirements related to climate, drought, land, livestock, crops, water, and weather. For each category of services, results from one case study are presented. The services will be distributed to the stakeholders and are expected to provide a continuous monitoring framework for early and accurate assessment of factors affecting food security in Africa.This paper is part of the AfriCultuReS project "Enhancing Food Security in African Agricultural Systems with the Support of Remote Sensing", which received funding from the European Union's Horizon 2020 Research and Innovation Framework Programme under grant agreement No. 77465

Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress

This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into ‘healthy and diseased plant classification’ with an emphasis on classification accuracy, early detection of stress, and disease severity. A central focus of the review is the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease. A summary of techniques used to detect biotic and abiotic stress in plants is presented, including the level of accuracy associated with each method

Sequential application of hyperspectral indices for delineation of stripe rust infection and nitrogen deficiency in wheat

© 2015, Springer Science+Business Media New York. Nitrogen (N) fertilization is crucial for the growth and development of wheat crops, and yet increased use of N can also result in increased stripe rust severity. Stripe rust infection and N deficiency both cause changes in foliar physiological activity and reduction in plant pigments that result in chlorosis. Furthermore, stripe rust produce pustules on the leaf surface which similar to chlorotic regions have a yellow color. Quantifying the severity of each factor is critical for adopting appropriate management practices. Eleven widely-used vegetation indices, based on mathematic combinations of narrow-band optical reflectance measurements in the visible/near infrared wavelength range were evaluated for their ability to discriminate and quantify stripe rust severity and N deficiency in a rust-susceptible wheat variety (H45) under varying conditions of nitrogen status. The physiological reflectance index (PhRI) and leaf and canopy chlorophyll index (LCCI) provided the strongest correlation with levels of rust infection and N-deficiency, respectively. When PhRI and LCCI were used in a sequence, both N deficiency and rust infection levels were correctly classified in 82.5 and 55 % of the plots at Zadoks growth stage 47 and 75, respectively. In misclassified plots, an overestimation of N deficiency was accompanied by an underestimation of the rust infection level or vice versa. In 18 % of the plots, there was a tendency to underestimate the severity of stripe rust infection even though the N-deficiency level was correctly predicted. The contrasting responses of the PhRI and LCCI to stripe rust infection and N deficiency, respectively, and the relative insensitivity of these indices to the other parameter makes their use in combination suitable for quantifying levels of stripe rust infection and N deficiency in wheat crops under field conditions

Foliar Disease Detection in the Field Using Optical Sensor Fusion

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 6 (2004): C. Bravo, D. Moshou, R. Oberti, J. West, A. McCartney, L. Bodria and H. Ramon. Foliar Disease Detection in the Field Using Optical Sensor Fusion. (December 2004)

A deep learning approach for anthracnose infected trees classification in walnut orchards

This paper presents a novel approach for the detection of disease-infected leaves on trees with the use of deep learning. Focus of this study was to build an accurate and fast object detection system that can identify anthracnose-infected leaves on walnut trees, in order to be used in real agricultural environments. Similar studies in the literature address the disease identification issue; however, so far, the detection was performed on single leaves which had been removed from trees, using images taken in controlled environment with clear background. A gap has been identified in the detection of infected leaves on tree-level in real-field conditions, an issue which is tackled in our study. Deep learning is an area of machine learning that can be proved particularly useful in the development of such systems. The latest developments in deep learning and object detection, points us towards utilizing and adapting the state-of-the-art single shot detector (SSD) algorithm. An object detector was trained to recognize anthracnose-infected walnut leaves and the trained model was applied to detect diseased trees in a 4 ha commercial walnut orchard. The orchard was initially inspected by domain experts identifying the infected trees to be used as ground truth information. Out of the 379 trees of the orchard, 100 were randomly selected to train the object detector and the remaining 279 trees were used to examine the effectiveness and robustness of the detector comparing the experts’ classification with the predicted classes of the system. The best inputs and hyper-parameter configuration for the trained model provided an average precision of 63% for the object detector, which correctly classified 87% of the validation tree dataset. These encouraging results indicate that the detector shows great potential for direct application in commercial orchards, to detect infected leaves on tree level in real field conditions, and categorize the trees into infected or healthy in real time. Thus, this system can consist an applicable solution for real-time scouting, monitoring, and decision making. © 2021 Elsevier B.V

Intelligent autonomous system for the detection and treatment of fungal diseases in arable crops

In this paper, the development phases of a ground-based real-time remote sensing system are described. The proposed system can be carried by tractors or robotic platforms. This prototype system makes possible the detection of plant diseases automatically in arable crops at an early stage of disease development, even before the diseases are visibly detectable, during field operations. The methodology uses differences in reflectance and fluorescence properties between healthy and diseased plants. Hyperspectral reflectance, fluorescence imaging, and multispectral imaging techniques were developed for simultaneous acquisition in the same canopy. New fluorescence acquisition techniques were developed, experimental platforms were constructed, and the advantage of using sensor fusion was proven. An intelligent multisensor fusion decision system based on neural networks was developed aiming at predicting the presence of diseases or plant stresses, in order to treat the diseases in a spatially variable way. A robust multi-sensor platform integrating optical sensing, GPS and a data processing unit was constructed and calibrated. The functionality of automatic disease sensing and detection devices is crucial in order to conceive a site-specific spraying strategy against fungal foliar diseases. Furthermore, field tests were carried out to optimise the functioning of the multi-sensor disease-detection device. An overview is provided on how disease presence data are processed in order to enable an automatic site-specific spraying strategy in winter wheat. Furthermore, mapping of diseases based on automated optical sensing and intelligent prediction provide a spatially variable recommendation for spraying