A Review of Computer Vision Technologies in Precision Agriculture

Abstract

Precision agriculture offers a promising solution to enhance crop productivity and sustainability amidst global agricultural challenges. This paper reviews the development and application of computer vision technologies in modern farming, with a focus on deep learning techniques such as Convolutional Neural Networks (CNNs), including Residual Network (ResNet), You Only Look Once (YOLO), and Segmentation Network (SegNet), applied to disease detection, weed classification, and crop health monitoring. The integration of Unmanned Aerial Vehicles (UAVs), robotics, and the Internet of Things (IoT) has significantly advanced agricultural efficiency. However, challenges such as data scarcity, computational limitations, and environmental variability continue to impede large-scale adoption. Emerging solutions, such as lightweight AI models, edge computing, and multi-source data fusion, offer potential pathways to overcome these hurdles. These innovations are critical for scaling, adapting, and sustaining precision agriculture technologies. This paper provides an overview of the current state of computer vision in precision agriculture, identifies key challenges, and outlines future research directions aimed at advancing the field.