AIMC Topic: Plant Diseases

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A dual-branch model combining convolution and vision transformer for crop disease classification.

PloS one
Computer vision holds tremendous potential in crop disease classification, but the complex texture and shape characteristics of crop diseases make disease classification challenging. To address these issues, this paper proposes a dual-branch model fo...

Ambiguity-aware semi-supervised learning for leaf disease classification.

Scientific reports
In deep learning, Semi-Supervised Learning is a highly effective technique to enhances neural network training by leveraging both labeled and unlabeled data. This process involves using a trained model to generate pseudo labels to the unlabeled sampl...

Improved Pine Wood Nematode Disease Diagnosis System Based on Deep Learning.

Plant disease
Pine wilt disease caused by the pine wood nematode, , has profound implications for global forestry ecology. Conventional PCR methods need long operating time and are complicated to perform. The need for rapid and effective detection methodologies to...

Deep learning based ensemble model for accurate tomato leaf disease classification by leveraging ResNet50 and MobileNetV2 architectures.

Scientific reports
Global food security depends on tomato growing, but several fungal, bacterial, and viral illnesses seriously reduce productivity and quality, therefore causing major financial losses. Reducing these impacts depends on early, exact diagnosis of diseas...

Prediction of barberry witches' broom rust disease using artificial intelligence models: a case study in South Khorasan, Iran.

Scientific reports
The South Khorasan Province in Iran is the main producer of seedless barberry, accounting for 98% of the country's production. This has led to significant economic growth in the region. However, the cultivation of barberry is threatened by the rust f...

Integrating advanced deep learning techniques for enhanced detection and classification of citrus leaf and fruit diseases.

Scientific reports
In this study, we evaluate the performance of four deep learning models, EfficientNetB0, ResNet50, DenseNet121, and InceptionV3, for the classification of citrus diseases from images. Extensive experiments were conducted on a dataset of 759 images di...

A deep learning-based approach for the detection of cucumber diseases.

PloS one
Cucumbers play a significant role as a greenhouse crop globally. In numerous countries, they are fundamental to dietary practices, contributing significantly to the nutritional patterns of various populations. Due to unfavorable environmental conditi...

A lightweight deep learning model for multi-plant biotic stress classification and detection for sustainable agriculture.

Scientific reports
Plant pathogens and pests hinder general plant health, resulting in poor agricultural yields and production. These threaten global food security and cause environmental and economic shortages. Amidst the available existing heavy deep learning (DL) mo...

Hybrid vision GNNs based early detection and protection against pest diseases in coffee plants.

Scientific reports
Agriculture is an essential foundation that supports numerous economies, and the longevity of the coffee business is of paramount significance. Controlling and safeguarding coffee farms from harmful pests, including the Coffee Berry Borer, Mealybugs,...

Advancing plant leaf disease detection integrating machine learning and deep learning.

Scientific reports
Conventional techniques for identifying plant leaf diseases can be labor-intensive and complicated. This research uses artificial intelligence (AI) to propose an automated solution that improves plant disease detection accuracy to overcome the diffic...