Construction of an Intelligent Analysis System for Crop Health Status Based on Drone Remote Sensing Data and CNN

The health of crops is of great consequence to the security of the global food supply and the sustainability of agricultural economies. To address the shortcomings of traditional monitoring methods, which are characterized by high labor intensity, low efficiency, and insufficient timeliness, this paper proposes an innovative intelligent analysis system. The system uses remote sensing data from drones and convolutional neural network technology to achieve efficient crop classification and accurate identification of pests and diseases. Specifically, the system adopts a multi-scale attention convolutional network to optimize crop classification, improves the cyclic consistent adversarial network for remote sensing image translation to enhance the dataset, and improves the lightweight MobileNet V2 for disease and pest recognition. The proposed method achieved an average F1 and intersection to union ratio of 94.67% and 89.14% for crop classification and recognition, respectively. When translating crop remote sensing images, the Frechette distance and kernel distance were 98.73 and 3.08, respectively. The translated image enhanced the dataset and improved recognition accuracy and convergence speed. When identifying crop pests and diseases, the accuracy and recall were 97.14% and 97.18%, respectively. The parameter count was reduced to 2.01MB, indicating superiority. This study solves the uncertainty in remote sensing data and the complexity and limited number of training samples for convolutional neural networks. This provides technical support for the transformation of agriculture towards intelligence and sustainability.