AIMC Topic: Plant Diseases

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Monochromatic LeafAdaptNet (MLAN): an adaptive approach to spinach leaf disease detection using monochromatic imaging.

World journal of microbiology & biotechnology
A country's economic growth heavily relies on agricultural productivity, specifically nutrition derived from vegetables and leafy greens. Spinach, abundant in iron, vitamins, and other essential nutrients, plays a vital role in maintaining the health...

Deep learning method for cucumber disease detection in complex environments for new agricultural productivity.

BMC plant biology
Cucumber disease detection under complex agricultural conditions faces significant challenges due to multi-scale variation, background clutter, and hardware limitations. This study proposes YOLO-Cucumber, an improved lightweight detection algorithm b...

A Multi-kernel CNN model with attention mechanism for classification of citrus plants diseases.

Scientific reports
One of the primary challenges leading to a significant reduction in agricultural production is the prevalence of diseases affecting citrus plants. Prevention and monitoring the spread of citrus plant diseases is crucial for maintaining citrus product...

Spatial attention-guided pre-trained networks for accurate identification of crop diseases.

Scientific reports
The maintenance of agricultural productivity is critically dependent on the efficient and accurate identification of plant diseases. As observed, the manual inspection to the illness is often inefficient and error-prone, particularly under conditions...

Monitoring and predicting cotton leaf diseases using deep learning approaches and mathematical models.

Scientific reports
Cotton, the backbone of global textile production, demands sustainable agricultural practices to ensure fiber, food, and environmental security. Cotton crop play an essential role in farming economies; however, production is sometimes affected by var...

AI and IoT-powered edge device optimized for crop pest and disease detection.

Scientific reports
Climate change exacerbates the challenges of maintaining crop health by influencing invasive pest and disease infestations, especially for cereal crops, leading to enormous yield losses. Consequently, innovative solutions are needed to monitor crop h...

AI-Accelerated Identification of Novel Antimicrobial Peptides for Inhibiting .

Journal of agricultural and food chemistry
Fusarium head blight caused by threatens global wheat production, causing substantial yield reduction and mycotoxin accumulation. This study harnessed machine learning to accelerate the discovery of antifungal peptides targeting this phytopathogen. ...

Multiclass semantic segmentation for prime disease detection with severity level identification in Citrus plant leaves.

Scientific reports
Agriculture provides the basics for producing food, driving economic growth, and maintaining environmental sustainability. On the other hand, plant diseases have the potential to reduce crop productivity and raise expenses, posing a risk to food secu...

Plant leaf disease detection using vision transformers for precision agriculture.

Scientific reports
Plant diseases cause major crop losses worldwide, making early detection essential for sustainable farming. Traditional methods need large training datasets, are expensive, and may overfit. In leaf image analysis, convolutional neural networks (CNNs)...

Integrating multi-omics and machine learning for disease resistance prediction in legumes.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Multi-omics assisted prediction of disease resistance mechanisms using machine learning has the potential to accelerate the breeding of resistant legume varieties. Grain legumes, such as soybean (Glycine max (L.) Merr.), chickpea (Cicer arietinum L.)...