Detection of Performance of Hybrid Rice Pot-Tray Sowing Utilizing Machine Vision and Machine Learning Approach

Abstract

Monitoring the performance of hybrid rice seeding is very important for the seedling production line to adjust the sowing amount of the seeding device. The objective of this paper was to develop a system for the real-time online monitoring of the performance of hybrid rice seeding based on embedded machine vision and machine learning technology. The embedded detection system captured images of pot trays that passed under the illuminant cabinet installed in the seedling production line. This paper proposed an algorithm for fixed threshold segmentation by analyzing the images with the exploratory analysis method. With the algorithm, the grid image and seed image were extracted from the pot tray image. The paper also proposed a method for obtaining pixel coordinates of gridlines from the grid image. Binary images of seeds were divided into small pieces, according to the pixel coordinates of gridlines. Each piece corresponded to a cell on the pot tray. By scanning the contours in each piece of the image to check whether there were seeds in the cell, the number of empty cells was counted and then used to calculate the missing rate of hybrid rice seeding. The seed number sowed in pot trays was monitored while using the machine learning approach. The experimental results demonstrated that it would consume 4.863 s for the device to process an image, which allowed for the detection of the missing rate and seed number in real-time at the rate of 500 trays per hour (7.2 s per tray). The average accuracy of the detection of missing rates of a seedling production line was 94.67%. The average accuracy of the detection of seed numbers was 95.68%.

Evaluation Model of Rice Seedling Production Line Seeding Quality Based on Deep Learning

“…By employing connected component detection, it calculates the number of seeds in each cell, providing valuable data for subsequent supplementary sowing. Dong Wenhao et al [5] developed a fixed threshold segmentation algorithm to differentiate grid images from seed images within seedling tray photographs. They also devised a technique to extract grid line pixel coordinates from grid images, which entails scanning contours within each image to inspect seed presence in cells, thereby calculating the number of misses and ultimately determining the missing rate of hybrid rice seeds.…”

“…The detection accuracy of the number of seeds, ranging from zero to three particles in each single connected region, reached 95%, while the detection accuracy of the number of seeds exceeding four particles in each single connected region was up to 90%. Dong Wenhao et al [5] introduced a fixed As indicated in Figure 14, segmenting the eight types of images into 24 × 42 grids has improved the model's ability to detect missed sowing areas in seedling trays compared to the 16 × 28 grid parameter. The model successfully identified areas that were previously undetected under the 16 × 28 setting.…”

“…The detection accuracy of the number of seeds, ranging from zero to three particles in each single connected region, reached 95%, while the detection accuracy of the number of seeds exceeding four particles in each single connected region was up to 90%. Dong Wenhao et al [5] introduced a fixed threshold segmentation algorithm, which examines whether there are seeds in each planting hole by scanning the contour of each small block in the image. This algorithm is then utilized to calculate the mis-seeding rate of hybrid rice seeding, achieving an average accuracy of 94.67% in mis-seeding rate detection.…”

Development of Seeding Rate Monitoring System Applicable to a Mechanical Pot-Seeding Machine

“…Therefore, several studies have been conducted on monitoring systems for automatically detecting these locations. Dong et al [13] developed a monitoring system capable of detecting missing rice seeds using machine vision and reached a detection accuracy of 94.67%. Bai et al [14] developed a monitoring system for corn seeds and improved the average detection accuracy to 97% by using RGB and hue saturation value (HSV) color space conversions.…”

Selection of Effective Software for the Analysis of the Fractional Composition of the Chaotic Seed Layer Using the Example of Oilseed Radish

“…The first group involves the use of appropriate software based on Visual Studio C ++ (Python, Java) and the OpenCV library (Li & He, 2017;The OpenCV Reference Manual, 2019;Wu et al, 2020) which can analyse all images obtained from a camera or by scanning. As a result of the analysis, the seeds are automatically identified by taking consecutive points around their perimeters and maximising or minimising the values in the following sequence: image loading; conversion to a 1-bit image (black seed on a white background); morphology analysis to remove noise and gaps; contouring by marking all the seeds in the image and calculating the length L, width B, area S and length of the perimeter P of the seed (Dong et al, 2019;Zhu et al, 2020). In this method, it is possible to place the seeds with a uniform distribution over the scan area and the ability to more clearly separate along the contours of its location The use of this method with the chaotic mixing of single-layer seed thickness consists of the formation of appropriate image preparation, its chromatic transformation and corresponding binarisation (Zilbergleit & Temruk, 2017;Wu et al, 2020).…”