A novel image measurement algorithm for common mushroom caps based on convolutional neural network

Applications of deep-learning approaches in horticultural research: a review

“…On the basis of the first type, the second type of method realizes two-stage deep-learning object detection combined with the region proposal method to achieve an improvement in the detection rate and an acceleration in the detection speed; this method type mainly includes the R-CNN 33 , 73 , Faster R-CNN 44 , 59 , 60 , 77 and Mask R-CNN 38 , 54 . The third type includes end-to-end, single-stage deep-learning object detection algorithms, which can directly return the categories and position borders of multiple objects, such as the YOLO 37 , 72 , 74 and SSD 56 methods. Based on these models, fruit yield can be automatically estimated 32 , 54 , 68 , flower and fruitlet thinning and other gardening operations can be automatically conducted 48 , 101 , and the early detection of plant stress can be accomplished 6 , 90 .…”

“…The property of a highly hierarchical structure along with the massive learning capability of deep-learning models enables them to carry out predictions and classifications particularly well with good flexibility and adaptability to a wide range of highly complicated data analysis tasks 28 . With the robust capability of automatic feature learning, many complex problems in the field of horticultural science can be solved in an effective and rapid way by utilizing deep-learning methods, includin g various recognition 29 – 31 , yield estimation 32 , 33 , quality detection 27 , 34 , stress phenotyping detection 35 , 36 , growth monitoring 37 , 38 , and other applications 39 , 40 . In the next section, we introduce these applications in detail.…”

“…Crop traits are important to plant breeders and producers for plant production management, as well as for making intelligent decisions about excellent genotype selection when yield traits or quality traits are used. The automatic intelligent collection of horticultural crop growth information in advance provides a good basis for planters to monitor growth and plan the harvesting timeline during the maturation of fruits and vegetables 37 , 48 . Lu and his team have proposed methods to localize mushrooms and track their growth 37 , 57 .…”

“…The automatic intelligent collection of horticultural crop growth information in advance provides a good basis for planters to monitor growth and plan the harvesting timeline during the maturation of fruits and vegetables 37 , 48 . Lu and his team have proposed methods to localize mushrooms and track their growth 37 , 57 . Lu et al 57 adopted the YOLO algorithm to localize mushrooms in an image and proposed a positioning correction method to modify the localization result.…”

“…Lu et al 57 adopted the YOLO algorithm to localize mushrooms in an image and proposed a positioning correction method to modify the localization result. After the mushrooms had been precisely localized, Lu et al 37 developed an image measurement system to record the diameter of the mushroom caps during the maturation period. The proposed algorithm (the YOLO-V3 + SP algorithm) was used to calculate the mushroom circles based on the images captured by a camera continuously and then to record the growth information of the mushroom caps; the method outperformed the current circle Hough transform method (OpenCV’s implementation) 37 .…”