CottonLeafVision: An Explainable and Robust Deep Learning Framework for Cotton Leaf Disease Classification
arXiv:2606.14686v1 Announce Type: cross Abstract: Globally, cotton is a highly economically beneficial crop, as the textile industry heavily depends on it. So, the precise identification and detection of cotton leaf disease is crucial for economic stability. The development goal of "CottonLeafVision" is to accurately classify and detect cotton leaf disease. With this goal, we have evaluated multiple pretrained Deep Convolutional Neural Networks, including DenseNet201, InceptionV3, and VGG19 on a publicly available cotton leaf disease image dataset. This image dataset includes seven classes, si
The proliferation of AI and deep learning research enables specialized applications like agricultural disease detection, making such developments timely given advancements in computer vision.
Precise and early detection of cotton leaf disease can significantly impact agricultural yields and economic stability in regions reliant on cotton, offering a valuable tool for food security and economic resilience.
The ability to accurately classify cotton leaf diseases using an explainable and robust deep learning framework improves early disease management and reduces crop losses, enhancing agricultural productivity.
- · Agricultural sector
- · Farmers
- · Textile industry
- · AI/Deep Learning researchers
- · Pesticide manufacturers (potentially reduced usage)
- · Manual disease inspection methods
Improved crop yields and reduced economic losses for cotton farmers worldwide due to earlier disease intervention.
Increased investment and adoption of AI-driven precision agriculture technologies across various crop types, leading to more efficient resource use.
Potential for new agricultural economic models based on optimized yield predictions and proactive disease management, influencing global commodity markets.
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Read at arXiv cs.AI