Hybrid YOLOv7–Efficient Net with SE Attention: An Advanced Framework for Soybean Disease Detection
DOI:
https://doi.org/10.22399/ijcesen.4893Keywords:
Soybean Disease Detection, Deep Learning, YOLOv7, EfficientNet, Squeeze-and-Excitation Modules, Smart AgricultureAbstract
Timely identification of soybean leaf diseases is critical for protecting crop health and sustaining agricultural productivity. Conventional diagnostic techniques often deliver inconsistent or low-accuracy results, creating a need for an automated, high-precision detection framework. This study presents a hybrid deep-learning model that integrates an optimized YOLOv7 detector with Efficient Net and Squeeze-and- Excitation (SE) modules to enhance both detection and classification of soybean diseases. Efficient Net serves as the backbone for rich multi scale feature extraction, while the SE module applies channel-wise attention to emphasize the most informative features and suppress irrelevant signals. Comprehensive experiments on a benchmark soybean disease dataset demonstrate that the proposed architecture achieves superior performance, surpassing 97 % in precision, recall, and F1-score, and operates in real t ime. These results indicate that the method is well suited for deployment in smart agriculture systems to enable rapid, accurate monitoring of crop health.
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