Minghua Li

Biotechnology Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, China
Author    Correspondence author
Legume Genomics and Genetics, 2025, Vol. 16, No. 4   
Received: 28 Jun., 2025    Accepted: 15 Aug., 2025    Published: 30 Aug., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Peas, as a globally significant legume crop, play a crucial role in food production and human nutrition. However, field pests and diseases, drought, high temperature and nutrient deficiency and other stresses seriously affect the yield and quality of peas. Timely and efficient stress monitoring is of great significance for ensuring pea production. This study reviews the advantages of unmanned aerial vehicle (UAV) remote sensing platforms in farmland stress monitoring, including high-throughput phenotypic acquisition and the roles of multispectral, RGB, and thermal infrared sensors. It also analyzes the applicability of commonly used deep learning models (CNN, RNN, Transformer) in crop stress detection. And the application progress of image classification, object detection, and semantic segmentation technologies in identifying crop stress types, a technical framework combining unmanned aerial vehicles and deep learning was constructed. The data collection and annotation process, data preprocessing and enhancement methods, as well as the pipeline for fusing multi-source images with deep learning models were expounded. The identification, degree classification and spatial distribution visualization of physiological stress (drought, nutrient deficiency, high temperature) and biological stress (diseases, pests) were mainly discussed. In the case of field stress detection of peas, the recognition performance of the deep learning model and its application value in field management decisions were verified. The combination of unmanned aerial vehicle (UAV) remote sensing and deep learning technology can achieve precise detection of field stress in peas. This study aims to provide strong support for precision agricultural management.

Pea; Stress monitoring; Unmanned aerial vehicle; Deep learning; Crop phenotype