IoT Meets Computer Vision: An Improved Detection of Tomato Pests and Diseases

Accurate detection of tomato pests and diseases is essential for efficient agriculture as global food demand continues to grow. Traditional methods often struggle with accurate localization and detection, highlighting the need for advanced solutions. Leveraging the Internet of Things (IoT), which facilitates interconnected devices for data acquisition and transmission, this study introduces the TPDDM algorithm to significantly enhance detection capabilities in tomato farming. To address existing challenges, we employ innovative techniques within TPDDM. Data augmentation and mosaic optimization methods are utilized to enrich the dataset and enhance image information, improving model robustness. Integration of a pre-trained Vision Transformer (ViT) into the YOLOX model boosts feature extraction and enriches the model's ability to detect subtle patterns. Additionally, the design of the Adaptive Multi-scale Feature Pyramid Network (AMFPN) enables effective fusion of features across different scales, optimizing target detection accuracy. Evaluation using comprehensive performance metrics demonstrates TPDDM's superiority over traditional approaches and other contemporary models. Real-world application of TPDDM on diverse datasets validates its high accuracy and practical utility in tomato pest and disease detection. This research underscores the transformative potential of IoT-driven technologies in advancing precision agriculture, offering scalable solutions to meet the demands of modern farming practices.