Advanced CNN Technique for Plant Health Monitoring: Drop Path, SE Block and Efficient Scaling

Abstract

The early detection of plant diseases is crucial for improving agricultural productivity, and convolutional neural networks (CNNs) have emerged as a leading tool for this task. Recent advancements in CNN architectures, such as the incorporation of DropPath and Squeeze-and-Excitation (SE) blocks, have significantly improved disease classification accuracy. The DropPath technique enhances model generalization by preventing overfitting, allowing CNNs to better adapt to diverse agricultural datasets. Additionally, the SE block refines feature extraction by adaptively recalibrating channel-wise feature responses, enabling the model to focus on important characteristics of plant leaves and suppressing irrelevant ones. These innovations increase the model's ability to distinguish between healthy and diseased leaves, making it more effective in real-world agricultural applications. When combined with compound scaling, these techniques reduce computational costs while enhancing performance. Hybrid models that integrate CNNs with other machine learning methods further improve disease detection. Overall, these advancements in CNN architecture provide efficient, scalable, and accurate solutions for plant health monitoring, ensuring timely interventions and minimizing crop losses due to diseases.