AIMC Topic: Plant Diseases

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Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique.

Scientific reports
The difficulty of collecting maize leaf lesion characteristics in an environment that undergoes frequent changes, suffers varying illumination from lighting sources, and is influenced by a variety of other factors makes detecting diseases in maize le...

YOLOv8-RMDA: Lightweight YOLOv8 Network for Early Detection of Small Target Diseases in Tea.

Sensors (Basel, Switzerland)
In order to efficiently identify early tea diseases, an improved YOLOv8 lesion detection method is proposed to address the challenges posed by the complex background of tea diseases, difficulty in detecting small lesions, and low recognition rate of ...

EResNet-SVM: an overfitting-relieved deep learning model for recognition of plant diseases and pests.

Journal of the science of food and agriculture
BACKGROUND: The accurate recognition and early warning for plant diseases and pests are a prerequisite of intelligent prevention and control for plant diseases and pests. As a result of the phenotype similarity of the hazarded plant after plant disea...

Optimized deep learning network for plant leaf disease segmentation and multi-classification using leaf images.

Network (Bristol, England)
Automatic detection of plant diseases is very imperative for monitoring the plants because they are one of the major concerns in the agricultural sector. Continuous monitoring can combat diseases of plants, which contribute to production loss. In the...

Application of a U-Net Neural Network to the Maize Pathosystem.

Phytopathology
Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can presen...

End-to-end multimodal 3D imaging and machine learning workflow for non-destructive phenotyping of grapevine trunk internal structure.

Scientific reports
Quantifying healthy and degraded inner tissues in plants is of great interest in agronomy, for example, to assess plant health and quality and monitor physiological traits or diseases. However, detecting functional and degraded plant tissues in-vivo ...

Using transfer learning-based plant disease classification and detection for sustainable agriculture.

BMC plant biology
Subsistence farmers and global food security depend on sufficient food production, which aligns with the UN's "Zero Hunger," "Climate Action," and "Responsible Consumption and Production" sustainable development goals. In addition to already availabl...

ScabyNet, a user-friendly application for detecting common scab in potato tubers using deep learning and morphological traits.

Scientific reports
Common scab (CS) is a major bacterial disease causing lesions on potato tubers, degrading their appearance and reducing their market value. To accurately grade scab-infected potato tubers, this study introduces "ScabyNet", an image processing approac...

Plant leaf infected spot segmentation using robust encoder-decoder cascaded deep learning model.

Network (Bristol, England)
Leaf infection detection and diagnosis at an earlier stage can improve agricultural output and reduce monetary costs. An inaccurate segmentation may degrade the accuracy of disease classification due to some different and complex leaf diseases. Also,...

Taylor Remora optimization enabled deep learning algorithms for percentage of pesticide detection in grapes.

Environmental science and pollution research international
In the world, grapes are considered as the most significant fruit, and it comprises various nutrients, like Vitamin C and it is utilized to produce wines and raisins. The major six general grape leaf diseases and pests are brown spots, leaf blight, d...