An IoT-enabled environmental informatics framework for real-time crop disease detection using attention-driven deep learning

Introduction Crop diseases are a major challenge in agriculture because their early and accurate identification is essential for reducing crop losses and improving productivity. The integration of the Internet of Things (IoT) with deep learning provides an effective approach for automated crop disease monitoring by enabling the collection and analysis of environmental parameters such as soil moisture, temperature, and humidity. However, accurate fine-grained disease identification remains challenging because of variations in disease symptoms, environmental conditions, and visual characteristics. Methods This study proposes an IoT-enabled deep learning framework for automated crop disease detection and classification. The collected crop images are pre-processed using scaling, normalization, and data augmentation techniques to improve image quality and model generalization. For efficient disease detection, a lightweight attention-driven convolutional neural network-based object detection approach, termed YOLO10-YSTs (You Only Look Once 10-Yield Surveillance Technology), is employed. The framework integrates the real-time detection capability of YOLO10 with an attention mechanism to emphasize disease-relevant regions and a stigmergy-based encoding strategy to enhance feature representation and extraction. In addition, a novel DBGU-LSTM (Dual-Bi-Gated Recurrent Unit with Long Short-Term Memory) architecture is developed for fine-grained crop disease classification. Results The experimental results demonstrate that the proposed framework provides effective disease detection, discriminative feature representation, and fine-grained classification. The DBGU-LSTM architecture achieves improved classification performance and training stability compared with the conventional deep learning models considered in the study. Overall, the proposed approach achieves a classification accuracy of 97.99%, demonstrating its effectiveness for automated crop disease recognition. Discussion and conclusion The results indicate that combining IoT-based environmental monitoring with attention-driven deep learning and recurrent feature-learning mechanisms can provide an effective solution for intelligent crop disease detection. The proposed framework enables accurate identification of disease patterns while supporting efficient and automated agricultural monitoring. Therefore, the approach has potential to assist farmers in early disease diagnosis, reduce crop losses, and improve agricultural productivity.