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Variable rate air-assisted spray based on real-time disease spot identification

文献类型： 外文期刊

作者： Zhang, Jinhui 1 ; Yin, Hao 1 ; Zhou, Liangfu 2 ; Gu, Chao 3 ; Qiu, Wei 1 ; Lv, Xiaolan 4 ; Sun, Hao 1 ; Yu, Hongfeng 1 ; Zhang, Zhengwei 1 ;

作者机构： 1.Nanjing Agr Univ, Coll Engn, Nanjing, Peoples R China

2.Nanjing Vocat Univ Ind Technol, Engn Technol Training Ctr, Nanjing, Peoples R China

3.Nanjing Agr Univ, Coll Hort, Nanjing, Peoples R China

4.Jiangsu Acad Agr Sci, Nanjing, Peoples R China

关键词： disease spot detection; complex scenarios; deep convolutional neural network models; control strategies; variable rate application systems

期刊名称：PEST MANAGEMENT SCIENCE （ 影响因子：4.462； 五年影响因子：4.688 ）

ISSN： 1526-498X

年卷期：

页码：

收录情况： SCI

摘要： Background Currently, the variable-rate application (VA) of agrochemicals on fruit trees is based on canopy volume and biomass. The canopy volume has a significant relationship with disease resistance and degree of disease incidence. Therefore, this study proposes a VA method that uses deep convolutional neural networks for real-time recognition of disease spots on pear trees. Furthermore, it specifies the limitations and application scenarios of the disease spot recognition. Field performance tests were conducted to verify the performance of the proposed VA system. Results The results showed a mean average precision, precision, and recall of 87.42%, 83.76%, and 87.23%, respectively. The spot recognition rate was 81.3% when the canopy sampling distance, spot diameter, and canopy porosity were 1.2 m, 4-8 mm, and 55.76%, respectively. The results indicate that the proposed VA system saved 51.9% spray volume compared to conventional methods while ensuring quality. Conclusion Compared to the traditional constant rate model, the proposed VA technology based on real-time disease spot identification can reduce spraying in nondiseased areas, thereby abandoning the previous saturation application practice and significantly reducing pesticide use. (c) 2022 Society of Chemical Industry.

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