A Deep Learning Methodology for the Early Identification of Chili Plant Diseases Utilizing SSD-ResNet-50 Architecture
The prompt identification and precise categorization of chili plant diseases are crucial for promoting sustainable agriculture and reducing crop losses due to pests and pathogens. This research presents an automated detection method using the Single Shot Detector (SSD) framework, with ResNet-50 as the backbone and a Feature Pyramid Network (FPN) to manage multi-scale feature representations. Dataset including 14,248 images of chili plants, categorized into six classes like Leaf Spot, Rotten Fruit, Healthy Fruit, Healthy Leaf, Mosaic Curl, and Yellowing, underwent preprocessing involving segmentation, resizing, and augmentation, followed by a division into 90% training data and 10% testing data. Transfer learning was implemented using a COCO-pretrained SSD ResNet-50 FPN model, enhanced with cosine-decay learning-rate scheduling and momentum optimization. The assessment results indicated an overall accuracy of 92.7%, with the highest F1-scores achieved for Healthy Fruit (0.965) and Rotten Fruit (0.967). Under the COCO evaluation protocol, the model achieved an mAP@0.5 of 91.5% and mAP@[0.5:0.95] of 65.2%. Model ran at approximately 30 FPS on an NVIDIA T4 GPU. Reduced precision values were noted for Leaf Spot (0.866) and Mosaic Curl (0.850), suggesting a propensity for misclassification due to visual similarities among disease symptoms. Nonetheless, all classes attained F1-scores exceeding 0.86, illustrating the robustness of the proposed model. Importantly, the SSD-ResNet-50 approach offers both efficiency and accuracy within a single pipeline, enabling rapid inference practical for real-world applications. These findings emphasize the potential of deep learning-based solutions to strengthen plant disease monitoring systems. In conclusion, SSD with ResNet-50 FPN provides an effective and scalable methodology for the automated identification of chili plant diseases, contributing directly to sustainable agriculture and improved crop management practices.