Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm

Xiaoyu Li,Lin Yao,Jun Wu,Lei Liu,Yuefeng Du

doi:10.3390/agriculture11121238

Abstract

At present, the wide application of the CNN (convolutional neural network) algorithm has greatly improved the intelligence level of agricultural machinery. Accurate and real-time detection for outdoor conditions is necessary for realizing intelligence and automation of corn harvesting. In view of the problems with existing detection methods for judging the integrity of corn kernels, such as low accuracy, poor reliability, and difficulty in adapting to the complicated and changeable harvesting environment, this paper investigates a broken corn kernel detection device for combine harvesters by using the yolov4-tiny model. Hardware construction is first designed to acquire continuous images and processing of corn kernels without overlap. Based on the images collected, the yolov4-tiny model is then utilized for training recognition of the intact and broken corn kernels samples. Next, a broken corn kernel detection algorithm is developed. Finally, the experiments are carried out to verify the effectiveness of the broken corn kernel detection device. The laboratory results show that the accuracy of the yolov4-tiny model is 93.5% for intact kernels and 93.0% for broken kernels, and the value of precision, recall, and F1 score are 92.8%, 93.5%, and 93.11%, respectively. The field experiment results show that the broken kernel rate obtained by the designed detection device are in good agreement with that obtained by the manually calculated statistic, with differentials at only 0.8%. This study provides a technical reference of a real-time method for detecting a broken corn kernel rate.

Highlights

Corn is an indispensable food crop for people; corn kernels are damaged in the process of harvesting, threshing, transport, and storage
The working parameters of the corn kernel direct harvester are shown in Table 1; the threshing cylinder speed was selected with 3 levels: 300, 350, and 400 r/min; the concave clearance was selected with 3 levels: 35, 40, and 45 mm, and the traveling speed was selected with 3 levels: 3.0, 3.5, and 4.0 km/h
In order to verify the working effect of the broken corn kernel detection device, a field experiment was carried out based on a corn grain direct harvester; the results of the broken corn kernel detection algorithm for groups 2, 3, 7, and 8 are captured and shown in Table 1 and Figure 8a–d

Summary

Introduction

Corn is an indispensable food crop for people; corn kernels are damaged in the process of harvesting, threshing, transport, and storage. Corn harvesters have lacked detection of the kernel breakage rate, and usually rely on the experience of drivers to control working parameters to avoid damaged corn kernels as much as possible. This method is extremely inefficient and unsatisfactory, and severely restricts the development of intelligent corn combine harvesters. Due to the extremely harsh working environment of corn harvesting, the large feed amount of corn ears, and the scattered distribution of dust, bracts, and damaged mandrels, it is difficult to detect the small corn kernels that accumulate, overlap, and shield in the vehicle-mounted environment, which is unfavorable to harvest efficiency

Methods

Results

Conclusion