Deep Learning Structure for Real-time Crop Monitoring Based on Neural Architecture Search and UAV

Abstract

Abstract Real-time monitoring of crop growth has become indispensable in modern agriculture, facilitating prompt detection of crop stress, diseases, and nutrient deficiencies by farmers. This study investigates the feasibility of leveraging unmanned aerial vehicles (UAVs) and deep learning algorithms for the real-time monitoring of Vicia faba L. crop growth stages, aimed at informing decisions related to irrigation, fertilization, and pest management. The study introduces a cutting-edge deep learning model tailored for accurate real-time monitoring of diverse growth stages based on neural architecture search (NAS). This model is benchmarked against seven other rigorously trained models using a diverse dataset of 2530 UAV-captured images, encompassing varied and complex lighting and background conditions. We meticulously fine-tuned the training parameters, closely examining and comparing the substantial performance of each model. Notably, the NAS-based architecture model proved outstanding results, achieving a precision rate of 95.80%, a recall rate of 98.80%, and a mAP@0.5_0.95 value of 71.30%. It strikes an optimal balance between precision, speed, and model size compared to alternative neural network models. The mean average precision (mAP) stands at 95.50%, and it maintains a refresh rate of 24.8 frames per second (FPS), all within a compact model size of 256 megabytes (MB). This chosen model achieves an impressive inference speed of 40.32 milliseconds per frame during testing with new images. This performance is underpinned by the current technology of the NVIDIA Quadro P1000, recognized for its high performance and significant pipelines/CUDA cores.