Year: 2025
Type: article
Abstract: Soybean leaf diseases significantly affect crop yields in Indian agriculture. Timely detection is crucial for managing these diseases effectively-ly. While traditional CNN models deliver high accuracy, they have significant computational demands and slower speeds, limiting real-time use. This study aims to create faster CNN models that effectively identify eight common soybean leaf diseases while being easier to train. The method uses a Separable-CNN with a Global Average Pooling (GAP) layer to reduce parameters and computing needs. The dataset used to train the Fast CNN and Enhanced CNN models included 40,000 images across eight disease categories. These models underwent evaluation using validation and test datasets with varying splits: 70-20-10, 65-25-10, and 60-30-10. The Enhanced CNN achieved 98.14% test accuracy, outperforming models like VGG16, ResNet152V2, and InceptionV3. The Fast CNN achieved 95.70% accuracy with reduced training time, making it suitable for real-time deployment. The confusion matrix showed high accuracy with few errors. The new CNN models accurately identify soybean leaf diseases and work efficiently. These models could help manage diseases quickly and improve AI tools in agriculture.
DOI: 10.14419/myqfsc31
Authors: Kalpesh Patel, Atul Patel
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4416827438
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