TY - JOUR
T1 - Tomato Fruit Detection and Counting in Greenhouses Using Deep Learning
AU - Afonso, Manya
AU - Fonteijn, Hubert
AU - Fiorentin, Felipe Schadeck
AU - Lensink, Dick
AU - Mooij, Marcel
AU - Faber, Nanne
AU - Polder, Gerrit
AU - Wehrens, Ron
PY - 2020/11/19
Y1 - 2020/11/19
N2 - 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.
AB - 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.
U2 - 10.3389/fpls.2020.571299
DO - 10.3389/fpls.2020.571299
M3 - Article
VL - 11
JO - Frontiers in Plant Science
JF - Frontiers in Plant Science
SN - 1664-462X
M1 - 571299
ER -