Camera-Based Depth Perception for Precision Agriculture: A Software-Defined Approach to 3D Scene Understanding

Abstract

Autonomous agricultural systems need strong three-dimensional perception skills to carry out specific tasks such as selective crop intervention, weed detection, and avoidance of obstacles. Existing solutions are based on active depth sensing (LiDAR/radar) that provides accuracy at the cost of significant economic, operational, and environmental factors limiting its use in a variety of farming environments. As this article will show, passive camera-based depth perception can perform equally as well at a fraction of the cost in hardware, power, and environmental sensitivity. The fundamental novelty is the systematic combination of multi-view stereo geometry, learning-based refinement, and confidence-conscious orchestration into a software-based system that is optimized to work in agricultural settings. This identification of passive imaging as the main sensing modality, as opposed to supporting active sensors, allows the ongoing performance improvement via algorithmic improvement and retrofit integration to already existing machinery, where redesigning hardware is economically infeasible. Multicrop species validation over several growth cycles using camera-only systems has shown that these systems can be used to provide acceptable depth accuracy, high temporal stability in the presence of mechanical vibration, high crop detection accuracy, and long field operation with long deployment cycles. These performance attributes, together with the huge economic benefits, make camera-based depth perception a viable and scalable alternative to active depth sensing as a means of precision agriculture.