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| 1 | Design and modelling of the full-feed peanut picking device with selfadaptive adjustable working clearance and feeding rate显示文摘To improve the declining performance of a full-feed peanut picking device or solve the mechanical failures that occur due to fluctuations in the feeding rate during operation,the 4HLJI-3000 peanut intelligent picking combine harvester,which is a picking device with a self-adaptive adjustment of the working clearance,was developed as the research object in this study.Moreover,the key components,such as the picking roller,concave plate sieve and clearance adjustment mechanism of the concave plate sieve,were designed and analysed.Through the force analysis of the concave plate sieve of the picking device,the mathematical model of the concave plate sieve displacement of the picking device and feeding rate was obtained.The software system for monitoring,storing and analysing the concave plate sieve displacement of the picking device based on EasyBuilder Pro was designed,and the road monitoring test of displacement variation of concave plate sieve of the picking device and feeding rate was carried out.The linear function,power function,exponential function,quadratic function,compound function,logarithmic function and cubic function fitting were used to perform regression analysis of the test results by using IBM SPSS software.The results showed that the cubic function model had a higher fitting precision,and its determination coefficient was 0.992.Model verification experiments were proposed,and the results showed that the established cubic function model had a good accuracy.The absolute deviation rate ranged from 0 to 4.83%,and the average deviation rate was 2.22%.The deviation rate increased with an increasing feeding rate.The field experiments also proved that there was a cubic function relationship between the feeding rate and concave plate sieve displacement,the measured concave plate sieve displacement deviation rate ranged from 0 to 6.19%,and the average deviation rate was 2.73%compared with the calculated results.This study can provide a reference for the optimization design of the structure of full-feeding picking devices for peanuts and other crops and the intelligent measurement and control of the feeding rates. | Shenying Wang Baoliang Peng Huichang Wu Zhichao Hu Dawei Sun Yongwei Wang Mingzhu Cao | 2023 | International Journal of Agricultural and Biological Engineering2023，16，6： | 0 |
| 2 | 冬季猕猴桃树单木骨架提取与冠层生长预测方法显示文摘[目的/意义]猕猴桃果树生长重叠明显,树冠结构复杂,利用传统方式无法实现果树单木骨架提取与冠层预测,为对密集栽培的猕猴桃果园进行高效无损监测并获取果树生长参数,本研究利用冬季简单树形进行骨架提取,并集成深度学习与数学形态学方法,提高单木骨架预测精度,提出了一种融合骨架信息的冠层分割方案。[方法方法]采用低成本无人机图像获取高分辨率数据支持,改进PSP-Net语义分割模型,引入数学形态学处理提取单木骨架并优化骨架连续性,以优化单木骨架为先验实现冠层分割。[结果与讨论]优化骨架提取精度可达95%以上,相较于传统方式精度提高约15.71%,像素准确率(Pixel Accuracy,PA)值达95.84%,平均交并比(Mean In-tersection over Union,MIo U)值达95.76%,冠层分割加权得分(Weighted F1 Score,WF1)达94.07%左右;而冠层预测像素准确率PA可达95%以上,冠层分割WF1达95.76%左右,与直接利用原始骨架相比,优化骨架提高了冠层分割的PA为13.2%,MIo U为10.9%,WF1为18.4%,显著改善了分割指标。[结论]该研究为高效监测猕猴桃园以获取果树数据提供了可靠技术支撑,并为高效、低成本的果园精细化管理提供了全新的技术方案,具有重要的应用前景。 | 李政凯 于嘉辉 潘时佳 贾泽丰 牛子杰 | 2023 | 智慧农业(中英文)2023，5，4： | 0 |
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