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Plant Diseases

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A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network.

Sensors (Basel, Switzerland)
Increasing grain production is essential to those areas where food is scarce. Increasing grain production by controlling crop diseases and pests in time should be effective. To construct video detection system for plant diseases and pests, and to bui...

Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network.

BMC bioinformatics
BACKGROUND: The occurrence of cotton pests and diseases has always been an important factor affecting the total cotton production. Cotton has a great dependence on environmental factors during its growth, especially climate change. In recent years, m...

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...