Abstract
Accurately detecting and counting fruits during plant growth using imaging and computer vision is of importance not only from the point of view of reducing labor intensive manual measurements of phenotypic information, but also because it is a critical step toward automating processes such as harvesting. Deep learning based methods have emerged as the state-of-the-art techniques in many problems in image segmentation and classification, and have a lot of promise in challenging domains such as agriculture, where they can deal with the large variability in data better than classical computer vision methods. This paper reports results on the detection of tomatoes in images taken in a greenhouse, using the MaskRCNN algorithm, which detects objects and also the pixels corresponding to each object. Our experimental results on the detection of tomatoes from images taken in greenhouses using a RealSense camera are comparable to or better than the metrics reported by earlier work, even though those were obtained in laboratory conditions or using higher resolution images. Our results also show that MaskRCNN can implicitly learn object depth, which is necessary for background elimination.
Highlights
Tomatoes are an economically important horticultural crop and the subject of research in seed development to improve yield
We report results on the detection of tomatoes from these images, using MaskRCNN trained with images in which foreground fruits are annotated
We evaluate the results of MaskRCNN on our validation set
Summary
Tomatoes are an economically important horticultural crop and the subject of research in seed development to improve yield. In recent years there has been great and increasing interest in automating agricultural processes like harvesting (Bac et al, 2014), pruning (Paulin et al, 2015), or localized spraying (Oberti et al, 2016). This has stimulated the development of image analysis and computer vision methods for the detection of fruits and vegetables. Since imaging is a quick and non-destructive way of measurement, detection of fruits, both ripe and unripe, and other plant traits using computer vision is useful for phenotyping (Minervini et al, 2015; Das Choudhury et al, 2019) and yield prediction. The number of fruits during plant growth is an important trait because it is an indicator of the expected yield, but is necessary for certain crops such as apple, where yield must be controlled to avoid biennial tree stress
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