Vision-Based a Seedling Selective Planting Control System for Vegetable Transplanter
Abstract
:1. Introduction
2. Materials and Methods
2.1. Seedlings and Seedling Tray
2.2. Seedling Picking Mechanism and Principle
2.3. Seedling Feeding Mechanism and Principle
2.4. Logic Execution Function Design
3. Seedling Selective Planting System
3.1. Image Acquisition Sense
3.2. Image Preprocessing
3.3. Image Segmentation Based on FCM
3.4. Visual Identification of Seedlings
- Step 1: The seedling image and preprocessing such as image cropping and compression are read;
- Step 2: Image mask operation, ROI region extracted;
- Step 3: Parameters such as c, m, and vi initialized, and the image feature segmented;
- Step 4: The membership of the cluster center uik and cluster center vi updated;
- Step 5: It is determined whether the optimal conditions or the maximum number of iterations are met, and one of the two is met, then Step 6 is executed; otherwise, return to Step 4 follows;
- Step 6: The threshold P of each ROI region calculated, the seedlings and labels identified;
- Step 7: Seedling selective plan identified and output.
4. Results and Discussions
4.1. Seedling Identification Test
4.1.1. Result of Seedling Identification
4.1.2. Discussion
4.2. Experiment of Seedling Selective Planting
4.2.1. Result of the Selective Planting Experiment
4.2.2. Discussion
- The selective planting system is an interactive system of seedlings, mechanical and visual control, has the agronomic integration attributes of agricultural machinery. Therefore, the growth morphology of seedlings is an important factor in improving the effect of selective transplanting [24], screen the combination of seedlings parameters suitable for selective planting, such as seedling age (leaf stretch), stem mechanical properties (moisture content), etc., and reduce the systematic misjudgment caused by leaf crossing.
- The misidentification of HS by selective planting system will cause waste of resources. The seedlings waste rate indicator should also be considered in the design and optimization of the system, as defined as follows:
5. Conclusions
- Based on the idea of humanoid selective transplanting, selecting suitable seedlings for transplanting and improving the efficiency of transplanting, this paper constructs a seedling selective planting control system applicable to vegetable transplanters. The seedling picking mechanism and seedling feeding mechanism are designed, and the selective planting scheme is proposed by combining machine vision and logic control technology.
- A fast framework of tray hole location and seedling identification (FHLSI) is proposed for the fast and accurate identification requirements of selective planting under transplanter working conditions. The visual recognition scene suitable for transplanting conditions is constructed using asymmetric light and the crossover of the CCD camera’s field of view, effectively suppressing background interference. A selective planting control system for seedlings was designed based on the intersection of mask and image operations combined with the FCM segmentation algorithm.
- The test results show that the proposed visual identification method achieves an average accuracy of 92.35% under working conditions, with a 15.4% improvement in transplanting quality with the seedling selective planting control system.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter | Picking Cylinder | Feeding Cylinder |
---|---|---|
Cylinder bore/mm | 25 | 16 |
Maximum open/mm | 28 | / |
Travel/mm | / | 150 |
Maximum push/kg | 6 | 8 |
Piston rod thread/mm | M5 | M5 |
Maximum clamp/kg | 3 | / |
Speed range/mm·s−1 | / | 50–750 |
Type | Leaf ROI (PLeaf) | Stem ROI (PStem) | Real Image |
---|---|---|---|
A | 0.5 ≤ P ≤ 1 | 0.08 ≤ P ≤ 0.2 | Healthy Seedling |
B | 0 ≤ P ≤ 0.5 | 0 ≤ P ≤ 0.08 | Empty Hole |
0.2 ≤ P ≤ 1 | Damaged Seedling |
Characteristics | Actual Class | Predict Class | Accuracy/% |
---|---|---|---|
HS | 970 | 945 | 97.4 |
EH | 500 | 407 | 81.4 |
DS | 420 | 398 | 94.8 |
OT | 30 | 23 | 76.7 |
Planting Status | Test Number | Seedling Feeding Number | Quality Qualified Number | Planting Qualified Rate/% |
---|---|---|---|---|
Group 1 | 1 | 128 | 102 | 79.6 |
2 | 128 | 97 | 75.8 | |
3 | 128 | 105 | 82.0 | |
4 | 128 | 99 | 77.3 | |
5 | 128 | 100 | 78.1 | |
Average: | 78.5 | |||
Group 2 | 1 | 103 | 95 | 92.2 |
2 | 108 | 101 | 93.5 | |
3 | 112 | 106 | 94.6 | |
4 | 109 | 104 | 95.4 | |
5 | 114 | 107 | 93.8 | |
Average: | 93.9 |
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Li, M.; Xiao, L.; Ma, X.; Yang, F.; Jin, X.; Ji, J. Vision-Based a Seedling Selective Planting Control System for Vegetable Transplanter. Agriculture 2022, 12, 2064. https://doi.org/10.3390/agriculture12122064
Li M, Xiao L, Ma X, Yang F, Jin X, Ji J. Vision-Based a Seedling Selective Planting Control System for Vegetable Transplanter. Agriculture. 2022; 12(12):2064. https://doi.org/10.3390/agriculture12122064
Chicago/Turabian StyleLi, Mingyong, Liqiang Xiao, Xiqiang Ma, Fang Yang, Xin Jin, and Jiangtao Ji. 2022. "Vision-Based a Seedling Selective Planting Control System for Vegetable Transplanter" Agriculture 12, no. 12: 2064. https://doi.org/10.3390/agriculture12122064