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Plant Diseases

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Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction.

Sensors (Basel, Switzerland)
Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is...

Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease.

Nature communications
Medicines and agricultural biocides are often discovered using large phenotypic screens across hundreds of compounds, where visible effects of whole organisms are compared to gauge efficacy and possible modes of action. However, such analysis is ofte...

Region-aggregated attention CNN for disease detection in fruit images.

PloS one
BACKGROUND: Diseases and pests have a profound effect on a yearly harvest and productivity in agriculture. A precise and accurate detection of the diseases and pests could facilitate timely treatment and management of the diseases and pests and lesse...

Competency of Neural Networks for the Numerical Treatment of Nonlinear Host-Vector-Predator Model.

Computational and mathematical methods in medicine
The aim of this work is to introduce a stochastic solver based on the Levenberg-Marquardt backpropagation neural networks (LMBNNs) for the nonlinear host-vector-predator model. The nonlinear host-vector-predator model is dependent upon five classes, ...

A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images.

Sensors (Basel, Switzerland)
Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identi...

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases.

Sensors (Basel, Switzerland)
In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects...

In-Field Automatic Detection of Grape Bunches under a Totally Uncontrolled Environment.

Sensors (Basel, Switzerland)
An early estimation of the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on ma...

A deep learning-integrated micro-CT image analysis pipeline for quantifying rice lodging resistance-related traits.

Plant communications
Lodging is a common problem in rice, reducing its yield and mechanical harvesting efficiency. Rice architecture is a key aspect of its domestication and a major factor that limits its high productivity. The ideal rice culm structure, including major_...

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning.

Sensors (Basel, Switzerland)
Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive...

Plant Disease Recognition: A Large-Scale Benchmark Dataset and a Visual Region and Loss Reweighting Approach.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Plant disease diagnosis is very critical for agriculture due to its importance for increasing crop production. Recent advances in image processing offer us a new way to solve this issue via visual plant disease analysis. However, there are few works ...