Strawberry Maturity Recognition Algorithm Combining Dark Channel Enhancement and YOLOv5

Abstract

Aiming at the problems of low accuracy of strawberry fruit picking and large rate of mispicking or missed picking, YOLOv5 combined with dark channel enhancement is proposed. In “Fengxiang” strawberry, the criterion of “bad fruit” is added to the conventional three criteria of ripeness, near-ripeness, and immaturity, because some of the bad fruits are close to the color of ripe fruits, but the fruits are small and dry. The training accuracy of the four kinds of strawberries with different ripeness is above 85%, and the testing accuracy is above 90%. Then, to meet the demand of all-day picking and address the problem of low illumination of images collected at night, an enhancement algorithm is proposed to enhance the images, which are recognized. We compare the actual detection results of the five enhancement algorithms, i.e., histogram equalization, Laplace transform, gamma transform, logarithmic variation, and dark channel enhancement processing under the different numbers of fruits, periods, and video tests. The results show that combined with dark channel enhancement, YOLOv5 has the highest recognition rate. Finally, the experimental results demonstrate that YOLOv5 is better than SSD, DSSD, and EfficientDet in terms of recognition accuracy, and the correct rate can reach more than 90%. Meanwhile, the method has good robustness in complex environments such as partial occlusion and multiple fruits.

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

“…The backbone for YOLOv5 is cross stage partial network (CSPNet) that is used to extract rich informative features from an input image and, by utilizing deeper networks, the processing time has been improved. YOLOv5 is implemented in detecting wheat spikes using UAV [155], detecting maturity of strawberry fruit [156], detecting defects of kiwi fruit [157] and detecting apple fruit [158].…”

High‐throughput phenotyping for breeding targets—Current status and future directions of strawberry trait automation

“…However, CNNs are reasonably robust to variance in illumination, and this can be further addressed through methods such as merging features from different colour spaces (Kirk et al., 2020). The unstructured, complex environment also poses challenges in terms of occlusion of the organs under evaluation by other fruit, flowers, stems or leaves, and cluttered backgrounds make segmentation difficult (Fan et al., 2022; Kirk et al., 2020; Lamb & Chuah, 2018; Lin & Chen, 2018; Yu et al., 2019; Zhou et al., 2020), but imaging from multiple viewpoints (Kerfs et al., 2017) and 3D sensing have the potential to assist with this (Le Louëdec & Cielniak, 2021a). Fruit characteristics, such as the small size of the fruit and variation in appearance, have also been noted as further obstacles in agricultural settings (Fan et al., 2022; Kirk et al., 2020), along with sensor‐related restrictions, such as available camera viewpoints, low contrast, variance in both colour balance and saturation and the interference of the sun on infra‐red based sensors (Heylen et al., 2021; Kirk et al., 2020; Le Louëdec & Cielniak, 2021a).…”

“…(2017), has been utilised in flower detection studies, as flowers are most visible from above the canopy. A consideration when using CNNs for high‐throughput phenotyping is how to balance the trade‐off between accuracy (for example, using Faster R‐CNN type networks (Chen et al., 2019; Lin & Chen, 2018; Zhou et al., 2020) and efficiency (such as the YOLO family of architectures (Fan et al., 2022; Kim et al., 2020; Zhang et al., 2022)) if real‐time performance is of relevance in the system. The automation of fruit and flower counts is beneficial to the selection process as the productivity of genotypes can be assessed, allowing breeders to immediately disregard those that do not meet the required level and thus increasing the selection process efficiency.…”

Identification of Metabolites Changes and Quality in Strawberry Fruit: Effect of Cultivation in High Tunnel and Open Field

“…By using greenhouse cultivation systems against adverse climatic conditions, it is possible to eliminate the problems that may occur in yield and quality increase. The use of greenhouse systems in strawberry production helps to protect plants from adverse environmental conditions and to spread the production over a long period of time, helping the producers to earn more income [ 5 , 6 , 7 ]. There are great differences in fruit quality of strawberry varieties grown in plastic tunnels or in the open field [ 8 ].…”