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Cabbage and Weed Identification Based on Machine Learning and Target Spraying System Design

文献类型： 外文期刊

作者： Zhao, Xueguan 1 ; Wang, Xiu 1 ; Li, Cuiling 1 ; Fu, Hao 1 ; Yang, Shuo 1 ; Zhai, Changyuan 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing, Peoples R China

2.Natl Engn Res Ctr Intelligent Equipment Agr, Beijing, Peoples R China

3.Guangxi Univ, Coll Mech Engn, Beijing, Peoples R China

关键词： target spraying; independent nozzle control; target identification; effective spraying rate; pesticide saving amount

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： The complexity of natural elements seriously affects the accuracy and stability of field target identification, and the speed of an identification algorithm essentially limits the practical application of field pesticide spraying. In this study, a cabbage identification and pesticide spraying control system based on an artificial light source was developed. With the image skeleton point-to-line ratio and ring structure features of support vector machine classification and identification, a contrast test of different feature combinations of a support vector machine was carried out, and the optimal feature combination of the support vector machine and its parameters were determined. In addition, a targeted pesticide spraying control system based on an active light source and a targeted spraying delay model were designed, and a communication protocol for the targeted spraying control system based on electronic control unit was developed to realize the controlled pesticide spraying of targets. According to the results of the support vector machine classification test, the feature vector comprised of the point-to-line ratio, maximum inscribed circle radius, and fitted curve coefficient had the highest identification accuracy of 95.7%, with a processing time of 33 ms for a single-frame image. Additionally, according to the results of a practical field application test, the average identification accuracies of cabbage were 95.0%, average identification accuracies of weed were 93.5%, and the results of target spraying at three operating speeds of 0.52 m/s, 0.69 m/s and 0.93 m/s show that the average invalid spraying rate, average missed spraying rate, and average effective spraying rate were 2.4, 4.7, and 92.9%, respectively. Moreover, it was also found from the results that with increasing speeds, the offset of the centre of the mass of the target increased and reached a maximum value of 28.6 mm when the speed was 0.93 m/s. The void rate and pesticide saving rate were 65 and 33.8% under continuous planting conditions and 76.6 and 53.3% under natural seeding deficiency conditions, respectively.

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