Welcome to Smart Agriculture

Smart Agriculture ›› 2022, Vol. 4 ›› Issue (4): 49-60.doi: 10.12133/j.smartag.SA202207004

• Topic--Smart Farming of Field Crops • Previous Articles     Next Articles

Infield Corn Kernel Detection and Counting Based on Multiple Deep Learning Networks

LIU Xiaohang1,2(), ZHANG Zhao1,2(), LIU Jiaying1,2, ZHANG Man1,2, LI Han1,2, FLORES Paulo3, HAN Xiongzhe4,5   

  1. 1.Key Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, China
    2.Key Lab of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China
    3.Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58012, United States
    4.Department of Biosystems Engineering, Kangwon National University, Chuncheon 24341, Korea
    5.Interdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon 24341, Korea
  • Received:2022-07-12 Online:2022-12-30
  • corresponding author: ZHANG Zhao, E-mail:zhaozhangcau@cau.edu.cn
  • About author:LIU Xiaohang, E-mail:lxhhaust@163.com
  • Supported by:
    Central University Basic Research Business Fee Special Fund Support (00112502); World's Top Agricultural University International Cooperation and Exchange Seed Fund (15052001)

Abstract:

Machine vision has been increasingly used for agricultural sensing tasks. The detection method based on deep learning for infield corn kernels can improve the detection accuracy. In order to obtain the number of lost corn kernels quickly and accurately after the corn harvest, and evaluate the corn harvest combine performance on grain loss, the method of directly using deep learning technology to count corn kernels in the field was developed and evaluated. Firstly, an RGB camera was used to collect image with different backgrounds and illuminations, and the datasets were generated. Secondly, different target detection networks for kernel recognition were constructed, including Mask R-CNN, EfficientDet-D5, YOLOv5-L and YOLOX-L, and the collected 420 effective images were used to train, verify and test each model. The number of images in train, verify and test datasets were 200, 40 and 180, respectively. Finally, the counting performances of different models were evaluated and compared according to the recognition results of test set images. The experimental results showed that among the four models, YOLOv5-L had overall the best performance, and could reliably identify corn kernels under different scenes and light conditions. The average precision (AP) value of the model for the image detection of the test set was 78.3%, and the size of the model was 89.3 MB. The correct rate of kernel count detection in four scenes of non-occlusion, surface mid-level-occlusion, surface severe-occlusion and aggregation were 98.2%, 95.5%, 76.1% and 83.3%, respectively, and F1 values were 94.7%, 93.8%, 82.8% and 87%, respectively. The overall detection correct rate and F1 value of the test set were 90.7% and 91.1%, respectively. The frame rate was 55.55 f/s, and the detection and counting performance were better than Mask R-CNN, EfficientDet-D5 and YOLOX-L networks. The detection accuracy was improved by about 5% compared with the second best performance of Mask R-CNN. With good precision, high throughput, and proven generalization, YOLOv5-L can realize real-time monitoring of corn harvest loss in practical operation.

Key words: harvest loss, infield corn kernel, deep learning, kernel count, YOLOv5-L, YOLOX-L, Mask R-CNN, EfficientDet-D5

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