Maize Harvesting Optimization: A Vision-Based Approach for Precise Cob Localization and Maturity Grading

Abstract

Interest and employment in agriculture are decreasing globally. In developing countries, with growing younger populations, the number of people involved in agriculture is decreasing. In Benin, one of the main reasons that agriculture is not attractive is the lack of machinery to support farm workers. As a first step towards the development of a robotic system for maize harvesting, this study proposes a two-stage vision pipeline: YOLO-based cob detection followed by maturity classification. Using field-collected maize images, we localize cobs. For maturity grading, MobileNetV3 is the baseline, compared with CLIP zero-shot and a Prototypical Network (few-shot), which gives the best results on our dataset.