Li Jiachao, Zhou Ya'nan, Zhang He, Pan Dayu, Gu Ying, Luo Bin
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
National Engineering Research Center for Information Technology in Agriculture, Beijing, China.
PeerJ Comput Sci. 2024 Aug 5;10:e2207. doi: 10.7717/peerj-cs.2207. eCollection 2024.
Plant height is a significant indicator of maize phenotypic morphology, and is closely related to crop growth, biomass, and lodging resistance. Obtaining the maize plant height accurately is of great significance for cultivating high-yielding maize varieties. Traditional measurement methods are labor-intensive and not conducive to data recording and storage. Therefore, it is very essential to implement the automated reading of maize plant height from measurement scales using object detection algorithms.
This study proposed a lightweight detection model based on the improved YOLOv5. The MobileNetv3 network replaced the YOLOv5 backbone network, and the Normalization-based Attention Module attention mechanism module was introduced into the neck network. The CioU loss function was replaced with the EioU loss function. Finally, a combined algorithm was used to achieve the automatic reading of maize plant height from measurement scales.
The improved model achieved an average precision of 98.6%, a computational complexity of 1.2 GFLOPs, and occupied 1.8 MB of memory. The detection frame rate on the computer was 54.1 fps. Through comparisons with models such as YOLOv5s, YOLOv7 and YOLOv8s, it was evident that the comprehensive performance of the improved model in this study was superior. Finally, a comparison between the algorithm's 160 plant height data obtained from the test set and manual readings demonstrated that the relative error between the algorithm's results and manual readings was within 0.2 cm, meeting the requirements of automatic reading of maize height measuring scale.
株高是玉米表型形态的重要指标，与作物生长、生物量和抗倒伏性密切相关。准确获取玉米株高对于培育高产玉米品种具有重要意义。传统测量方法劳动强度大，不利于数据记录和存储。因此，利用目标检测算法实现从测量刻度上自动读取玉米株高非常必要。
本研究提出了一种基于改进YOLOv5的轻量级检测模型。用MobileNetv3网络替换YOLOv5主干网络，并在颈部网络中引入基于归一化的注意力模块注意力机制模块。用EioU损失函数替换CioU损失函数。最后，使用一种组合算法实现从测量刻度上自动读取玉米株高。
改进后的模型平均精度达到98.6%，计算复杂度为1.2 GFLOPs，内存占用1.8 MB。在计算机上的检测帧率为54.1 fps。通过与YOLOv5s、YOLOv7和YOLOv8s等模型比较，明显看出本研究中改进模型的综合性能更优。最后，将算法从测试集中获得的160个株高数据与人工读数进行比较，结果表明算法结果与人工读数之间的相对误差在0.2 cm以内，满足玉米株高测量刻度自动读取的要求。
