No abstract

No abstract

Real-time crop harvest data acquisition from harvesters during harvesting operations is an important way to understand the distribution of crop harvest in the field. Most real-time monitoring systems for grain yield using sensors are vulnerable to factors such as low accuracy and low real-time performance. To address this phenomenon, a real-time grain yield monitoring system was designed in this study. The real-time monitoring of yield was accomplished by adding three pairs of photoelectric sensors to the elevator of the corn kernel harvester. The system mainly consists of a signal acquisition and processing module, a positioning module and a visualization terminal; the signal acquisition frequency was set to 1 kHz and the response time was 2 ms. When the system operated, the signal acquisition and processing module detected the sensor signal duration of grain blocking the scrapers of the grain elevator in real-time and used the low-potential signal-based corn grain yield calculation model constructed in this study to complete the real-time yield measurement. The results of the bench tests, conducted under several different operating conditions with the simulated elevator test bench built, showed that the error of the system measurement was less than 5%. Field tests were conducted on a Zoomlion 4YZL-5BZH combined corn kernel harvester and the results showed that the average error of measured yield was 3.72%. Compared to the yield measurement method using the weighing method, the average error of the bench test yield measurement was 7.6% and the average error of yield measurement in field trials with a mass flow sensor yield measurement system was 16.38%. It was verified that the system designed in this study has high yield measurement accuracy and real-time yield measurement, and can provide reference for precision agriculture and high yield management.

As the basic link of autonomous navigation in agriculture, crop row detection is vital to achieve accurate detection of crop rows for autonomous navigation. Machine vision algorithms are easily affected by factors such as changes in field lighting and weather conditions, and the majority of machine vision algorithms detect early periods of crops, but it is challenging to detect crop rows under high sheltering pressure in the middle and late periods. In this paper, a crop row detection algorithm based on LiDAR is proposed that is aimed at the middle and late crop periods, which has a good effect compared with the conventional machine vision algorithm. The algorithm proposed the following three steps: point cloud preprocessing, feature point extraction, and crop row centerline detection. Firstly, dividing the horizontal strips equally, the improved K-means algorithm and the prior information of the previous horizontal strip are utilized to obtain the candidate points of the current horizontal strip, then the candidate points information is used to filter and extract the feature points in accordance with the corresponding threshold, and finally, the least squares method is used to fit the crop row centerlines. The experimental results show that the algorithm can detect the centerlines of crop rows in the middle and late periods of maize under the high sheltering environment. In the middle period, the average correct extraction rate of maize row centerlines was 95.1%, and the average processing time was 0.181 s; in the late period, the average correct extraction rate of maize row centerlines was 87.3%, and the average processing time was 0.195 s. At the same time, it also demonstrates accuracy and superiority of the algorithm over the machine vision algorithm, which can provide a solid foundation for autonomous navigation in agriculture.

No abstract