Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

Abstract

Advancements in deep learning and computer vision have led to the discovery of numerous effective solutions to challenging problems in the field of agricultural automation. With the aim to improve the detection precision in the autonomous harvesting process of green asparagus, in this article, we proposed the DA-Mask RCNN model, which utilizes the depth information in the region proposal network. Firstly, the deep residual network and feature pyramid network were combined to form the backbone network. Secondly, the DA-Mask RCNN model added a depth filter to aid the softmax function in anchor classification. Afterwards, the region proposals were further processed by the detection head unit. The training and test images were mainly acquired from different regions in the basin of the Yangtze River. During the capturing process, various weather and illumination conditions were taken into account, including sunny weather, sunny but overshadowed conditions, cloudy weather, and daytime greenhouse conditions as well as nighttime greenhouse conditions. Performance experiments, comparison experiments, and ablation experiments were carried out using the five constructed datasets to verify the effectiveness of the proposed model. Precision, recall, and F1-score values were applied to evaluate the performances of different approaches. The overall experimental results demonstrate that the balance of the precision and speed of the proposed DA-Mask RCNN model outperform those of existing algorithms.

Mobile Robot System for Selective Asparagus Harvesting

“…The results from the study indicate that the recognition rate in laboratory tests was 94%, which is comparable to [33]; however, six spears were not recognized, because there were instances in which spears were placed too closely together, causing the algorithm to mistake two separate spears for a single spear. This issue, of detecting a single spear as opposed to multiple spears, was also responsible for the failure to collect some spears.…”

“…Hong et al [32] utilized an improved YOLOv5 algorithm for asparagus recognition and detection in complex environments, achieving an average mean precision of nearly 99% percent. Lastly, Liu et al [33] introduced depth-aided mask RCNN for asparagus detection, demonstrating a better balance between precision and speed than existing algorithms.…”

A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture

“…Beyond architectures, the training process must also account for greenhouse-specific factors. Models like those proposed by Cong et al [99], Gang et al [92], and Liu et al [112] illustrate the benefits of customized training regimes using specialized greenhouse datasets over pre-trained networks. However, collecting exhaustive labelled greenhouse data can be challenging.…”

“…Recent innovations by Liu et al [112] explored the application of the DA-Mask RCNN model for detecting green asparagus. The aim was to enhance detection precision during the autonomous harvesting of green asparagus by integrating MASK RCNN with depth information.…”