Smart Agriculture ›› 2024, Vol. 6 ›› Issue (6): 85-95.doi: 10.12133/j.smartag.SA202406013
• Topic--Intelligent Agricultural Knowledge Services and Smart Unmanned Farms(Part 1) • Previous Articles Next Articles
ZHANG Hui1(), HU Jun1,2(
), SHI Hang1,2, LIU Changxi1,2, WU Miao1,2
Received:
2024-06-27
Online:
2024-11-30
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ZHANG Hui, HU Jun, SHI Hang, LIU Changxi, WU Miao. Precision Target Spraying System Integrated with Remote Deep Learning Recognition Model for Cabbage Plant Centers[J]. Smart Agriculture, 2024, 6(6): 85-95.
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URL: https://www.smartag.net.cn/EN/10.12133/j.smartag.SA202406013
Table 4
Study on spray amount acquisition of target spray control system
喷头型号 | 喷雾压力/MPa | 速度/(km/h) | 频率次/s | 对靶实际喷雾量/L | 对靶需求喷雾量/L | 相对误差/% | |||
---|---|---|---|---|---|---|---|---|---|
单次喷雾量 | 1 min喷雾量 | 单次喷雾量 | 1 min喷雾量 | 单次喷雾量 | 1 min喷雾量 | ||||
ST110-04 | 0.20 | 3.6 | 1 | 0.014 | 0.86 | 0.010 8 | 0.65 | 30.0 | 32.0 |
ST110-06 | 0.40 | 7.2 | 2 | 0.012 | 1.36 | 0.011 2 | 1.35 | 7.1 | 0.7 |
ST110-10 | 0.30 | 10.8 | 3 | 0.010 | 1.58 | 0.010 4 | 1.95 | 3.8 | 19.0 |
ST110-15 | 0.25 | 14.4 | 4 | 0.010 | 2.76 | 0.006 1 | 2.95 | 63.9 | 6.4 |
Table 5
Target spray test results of plant core coverage of Chinese cabbage
喷头型号 | 喷雾压力/MPa | 速度/(km/h) | 单颗识别耗时/ms | 整体耗时/ms | 平均覆盖率/% | 实际喷雾量/mL | 药液需求量/mL |
---|---|---|---|---|---|---|---|
ST110-04 | 0.20 | 3.6 | 38.6 | 402 | 88 | 15.6 | 10.8 |
ST110-06 | 0.40 | 7.2 | 37.9 | 398 | 86 | 13.5 | 11.2 |
ST110-10 | 0.30 | 10.8 | 38.5 | 400 | 82 | 10.2 | 10.4 |
ST110-15 | 0.25 | 14.4 | 37.6 | 396 | 77 | 5.7 | 6.1 |
1 |
|
2 |
魏小春, 原玉香, 李林, 等. 大白菜抗干烧心病研究进展[J]. 中国瓜菜, 2022, 35(12): 1-6.
|
|
|
3 |
|
4 |
杨征鹤, 杨会民, 喻晨, 等. 设施蔬菜自动对靶喷药技术研究现状与分析[J]. 新疆农业科学, 2021, 58(8): 1547-1557.
|
|
|
5 |
|
6 |
|
7 |
|
8 |
|
9 |
|
10 |
|
11 |
|
12 |
|
13 |
|
14 |
赵春江, 范贝贝, 李瑾, 等. 农业机器人技术进展、挑战与趋势[J]. 智慧农业(中英文), 2023, 5(4): 1-15.
|
|
|
15 |
赵学观, 郑申玉, 易克传, 等. 考虑喷雾高度的大田蔬菜对靶喷雾系统设计与试验[J]. 农业工程学报, 2022, 38(11): 1-11.
|
|
|
16 |
权龙哲, 王建森, 奚德君, 等. 靶向灭草机器人药液喷洒空气动力学模型建立与验证[J]. 农业工程学报, 2017, 33(15): 72-80.
|
|
|
17 |
|
18 |
|
19 |
孙竹,顾伟,崔龙飞,等. 智能植保装备关键技术研究现状与发展趋势 [J]. 智能化农业装备学报(中英文), 2024, 5 (4): 1-23.
|
|
|
20 |
|
21 |
|
22 |
|
23 |
|
24 |
孔维永. 关于盘州市大白菜常见病虫害防治的思考[J]. 种子科技, 2024, 42(10): 109-111.
|
|
|
25 |
|
26 |
农林机械 在用喷雾机的检测 第2部分: 水平喷杆式喷雾机: GB/T 32250.2—2022 [S]. 北京: 中国标准出版社, 2022.
|
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