AIMC Topic: Plant Diseases

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Bioactive-guided structural optimization of 1,2,3-triazole phenylhydrazones as potential fungicides against Fusarium graminearum.

Pesticide biochemistry and physiology
The phytopathogenic fungus Fusarium graminearum is the major causal agent of fusarium head blight (FHB), which is one of the most serious diseases in wheat. Based on our previous work, the 1,2,3-triazole phenylhydrazone scaffold was further optimized...

Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques.

Computational intelligence and neuroscience
This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the typ...

Investigating potato late blight physiological differences across potato cultivars with spectroscopy and machine learning.

Plant science : an international journal of experimental plant biology
Understanding plant disease resistance is important in the integrated management of Phytophthora infestans, causal agent of potato late blight. Advanced field-based methods of disease detection that can identify infection before the onset of visual s...

Predicting rice blast disease: machine learning versus process-based models.

BMC bioinformatics
BACKGROUND: In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and Water Accounting Rice Model (WARM)) and two approaches based on machine learning algorithms (M5Rules and Recurrent...

Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network.

Sensors (Basel, Switzerland)
Plant leaf diseases are closely related to people's daily life. Due to the wide variety of diseases, it is not only time-consuming and labor-intensive to identify and classify diseases by artificial eyes, but also easy to be misidentified with having...

Recognition pest by image-based transfer learning.

Journal of the science of food and agriculture
BACKGROUND: Plant pests mainly refers to insects and mites that harm crops and products. There are a wide variety of plant pests, with wide distribution, fast reproduction and large quantity, which directly causes serious losses to crops. Therefore, ...

Quantifying the effect of Jacobiasca lybica pest on vineyards with UAVs by combining geometric and computer vision techniques.

PloS one
With the increasing competitiveness in the vine market, coupled with the increasing need for sustainable use of resources, strategies for improving farm management are essential. One such effective strategy is the implementation of precision agricult...

Rice Blast Disease Recognition Using a Deep Convolutional Neural Network.

Scientific reports
Rice disease recognition is crucial in automated rice disease diagnosis systems. At present, deep convolutional neural network (CNN) is generally considered the state-of-the-art solution in image recognition. In this paper, we propose a novel rice bl...

Field evaluation of an unmanned aerial vehicle (UAV) sprayer: effect of spray volume on deposition and the control of pests and disease in wheat.

Pest management science
BACKGROUND: Unmanned aerial vehicles (UAVs) are a recently developed aerial spraying technology. However, the effect of spray volume variation on deposition and pesticide control efficacy is unknown. The effect of three UAV spray volumes (9.0, 16.8 a...

Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination.

Sensors (Basel, Switzerland)
, a smut fungus, is studied as an agent for the biological control of (milk thistle) weed. Confirmation of the systemic infection is essential in order to assess the effectiveness of the biological control application and assist decision-making. Non...