What is it about?
This work focuses on the use of Artificial Neural Networks (ANNs) to automatically classify plant leaf diseases and evaluate detection performance. By training the ANN on features extracted from leaf images, the system can identify patterns linked to specific diseases and provide accurate, automated diagnosis. The research aims to enhance disease detection precision and reduce dependency on manual inspection.
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Why is it important?
Agricultural productivity: Plant diseases are a major cause of reduced crop yields worldwide. Accurate and timely detection can help farmers take preventive measures early. Cost-effectiveness: Automated ANN-based systems reduce the need for expensive lab-based testing or expert supervision in rural/agricultural communities. Food security: By minimizing crop loss, such systems contribute to ensuring sustainable food supply chains. Scalability: Once trained, ANN models can be deployed on mobile devices or drones, making them useful in large-scale agricultural monitoring
Perspectives
Academic perspective: Strengthens research in AI-driven agriculture, image classification, and deep learning applications in biological sciences. Industrial perspective: Offers scope for smart farming solutions that can integrate with IoT devices, precision agriculture platforms, and agri-tech startups. Societal perspective: Supports farmers and communities by reducing crop losses, lowering costs, and promoting food security—helping bridge the gap between technology and grassroots farming practices.
Venkatrao Palacherla
Godavari Global University
Read the Original
This page is a summary of: Employing ANN to classify and evaluate plant leaf disease detection effectiveness in order to increase accuracy, January 2025, American Institute of Physics,
DOI: 10.1063/5.0297037.
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