Abstract
The harvesting operation is a recurring task in the production of any crop, thus making it an excellent candidate for automation. In protected horticulture, one of the crops with high added value is tomatoes. However, its robotic harvesting is still far from maturity. That said, the development of an accurate fruit detection system is a crucial step towards achieving fully automated robotic harvesting. Deep Learning (DL) and detection frameworks like Single Shot MultiBox Detector (SSD) or You Only Look Once (YOLO) are more robust and accurate alternatives with better response to highly complex scenarios. The use of DL can be easily used to detect tomatoes, but when their classification is intended, the task becomes harsh, demanding a huge amount of data. Therefore, this paper proposes the use of DL models (SSD MobileNet v2 and YOLOv4) to efficiently detect the tomatoes and compare those systems with a proposed histogram-based HSV colour space model to classify each tomato and determine its ripening stage, through two image datasets acquired. Regarding detection, both models obtained promising results, with the YOLOv4 model standing out with an F1-Score of 85.81%. For classification task the YOLOv4 was again the best model with an Macro F1-Score of 74.16%. The HSV colour space model outperformed the SSD MobileNet v2 model, obtaining results similar to the YOLOv4 model, with a Balanced Accuracy of 68.10%.
Highlights
The high labour demand for the execution of several agricultural tasks causes bottlenecks within farms’ organisation with associated efficiency costs, especially in recurrent situations of unavailability of labour
This context demands the adoption of new technologies and the search for solutions that improve cost reduction or compensate for the lack of labour to guarantee the success of the various production systems
The results presented below refer to the benchmarking of the test set that allows understanding the generalisation capacity of the trained Deep Learning (DL) models
Summary
The high labour demand for the execution of several agricultural tasks causes bottlenecks within farms’ organisation with associated efficiency costs, especially in recurrent situations of unavailability of labour. Cost reduction is hindered by the vital needs for labour power [2]. This context demands the adoption of new technologies and the search for solutions that improve cost reduction or compensate for the lack of labour to guarantee the success of the various production systems. Increasing efficiency and reducing labour dependency in this operation could ensure higher yields and competitiveness in high-tech food production, so the development of harvesting robots should be considered as a viable alternative [5]
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