Object Detection in Tomato Greenhouses: A Study on Model Generalization

Sammar Haggag, Matthew Veres, Cole Tarry and Medhat Moussa ()
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Sammar Haggag: School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
Matthew Veres: School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
Cole Tarry: School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
Medhat Moussa: School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada

Agriculture, 2024, vol. 14, issue 2, 1-12

Abstract: Harvesting operations in agriculture are labour-intensive tasks. Automated solutions can help alleviate some of the pressure faced by rising costs and labour shortage. Yet, these solutions are often difficult and expensive to develop. To enable the use of harvesting robots, machine vision must be able to detect and localize target objects in a cluttered scene. In this work, we focus on a subset of harvesting operations, namely, tomato harvesting in greenhouses, and investigate the impact that variations in dataset size, data collection process and other environmental conditions may have on the generalization ability of a Mask-RCNN model in detecting two objects critical to the harvesting task: tomatoes and stems. Our results show that when detecting stems from a perpendicular perspective, models trained using data from the same perspective are similar to one that combines both perpendicular and angled data. We also show larger changes in detection performance across different dataset sizes when evaluating images collected from an angled camera perspective, and overall larger differences in performance when illumination is the primary source of variation in the data. These findings can be used to help practitioners prioritize data collection and evaluation efforts, and lead to larger-scale harvesting dataset construction efforts.

Keywords: deep learning; generalization; object detection; greenhouse (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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