AI-driven drone technology and computer vision for early detection of crop disease in large agricultural areas.

Timely detection of crop diseases in large, heterogeneous agricultural fields is difficult, as aerial imagery is often corrupted by illumination, weather, and crop-stage variations. This paper introduces AgroVisionNet, an AI-powered drone and computer vision approach that synthesises high-resolution drone imagery with in-field IoT/environmental sensor data to enhance early disease detection. The core of the proposed model is a hybrid CNN-Transformer backbone to extract spatial and contextual data from drone images, and an adaptive fusion layer to fuse time-aligned sensor readings and to make a decision, using visual and environmental evidence. Particularly, a multimodal drone–sensor dataset is collected across multiple crops and field conditions. Beyond widely used deep models for plant/crop disease identification, such as VGG16, ResNet50, Inception V3, and DenseNet121, experiments are conducted using the same training and evaluation framework. It is shown that AgroVisionNet achieves higher classification accuracy and F1-score, while inference remains feasible on an NVIDIA Jetson Nano using TensorFlow Lite. Moreover, by generating Grad-CAM plots, the study demonstrates that the proposed approach identifies disease-affected areas and, in this sense, provides interpretable information required by agronomists. These outcomes suggest that AI-based crop health tracking can be robust and field-ready by integrating drone imagery, sensor fusion, and edge computing.