Towards a Machine Vision-Based Yield Monitor for the Counting and Quality Mapping of Shallots

Abstract

In comparison to field crops such as cereals, cotton, hay and grain, specialty crops often require more resources, are usually more sensitive to sudden changes in growth conditions and are known to produce higher value products. Providing quality and quantity assessment of specialty crops during harvesting is crucial for securing higher returns and improving management practices. Technical advancements in computer and machine vision have improved the detection, quality assessment and yield estimation processes for various fruit crops, but similar methods capable of exporting a detailed yield map for vegetable crops have yet to be fully developed. A machine vision-based yield monitor was designed to perform size categorization and continuous counting of shallots in-situ during the harvesting process. Coupled with a software developed in Python, the system is composed of a video logger and a global navigation satellite system. Computer vision analysis is performed within the tractor while an RGB camera collects real-time video data of the crops under natural sunlight conditions. Vegetables are first segmented using Watershed segmentation, detected on the conveyor, and then classified by size. The system detected shallots in a subsample of the dataset with a precision of 76%. The software was also evaluated on its ability to classify the shallots into three size categories. The best performance was achieved in the large class (73%), followed by the small class (59%) and medium class (44%). Based on these results, the occasional occlusion of vegetables and inconsistent lighting conditions were the main factors that hindered performance. Although further enhancements are envisioned for the prototype system, its modular and novel design permits the mapping of a selection of other horticultural crops. Moreover, it has the potential to benefit many producers of small vegetable crops by providing them with useful harvest information in real-time.