| 采用局部点云和BP神经网络的苹果树剪枝决策系统构建 |
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| 引用本文： | 李鑫星,梁步稳,刘诗阳,李辉.采用局部点云和BP神经网络的苹果树剪枝决策系统构建[J].农业工程学报,2021,37(2):170-176. |
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| 作者姓名： | 李鑫星 梁步稳 刘诗阳 李辉 |
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| 作者单位： | 1.中国农业大学信息与电气工程学院，100083；2.农业农村部农业信息化标准化重点实验室，北京 100083 |
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| 基金项目： | 北京工商大学北京市重点实验室开放课题（BUBD-2017KF-09） |
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| 摘 要： | 针对当前果树智能化剪枝决策研究尚不完善的问题,以树形分析和人工智能剪枝决策为基础,建立苹果树剪枝决策系统。提出基于局部点云的树枝三维骨骼提取方法,该方法采用Harris角点检测、凝聚层次、深度层次分析算法提取三维骨骼关键点,并基于线覆盖法建立树枝的空间向量,获取苹果树枝的三维空间形态特征数据,从而生成树枝的三维骨骼图,实现真实树枝的数字化模拟;提出基于BP神经网络的剪枝决策方法,以三维骨骼图为特征,实现根据输入的果树数据自动分析并生成剪枝方案。结果表明,剪枝决策方案对于背上枝和向心枝的辨别程度较好,准确率均达到90%以上,对干扰枝的总检出率85.71%,整体符合要求。该系统实现了苹果树剪枝环节的数字化处理和智能化剪枝,为果树科学剪枝提供可靠的工具。
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| 关 键 词： | 智能系统 农业工程 苹果剪枝 三维点云 BP神经网络 |
| 收稿时间： | 2020/8/14 0:00:00 |
| 修稿时间： | 2020/12/21 0:00:00 |
Pruning decision system for apple tree based on local point cloud and BP neural network |
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| Li Xinxing,Liang Buwen,Liu Shiyang,Li Hui.Pruning decision system for apple tree based on local point cloud and BP neural network[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(2):170-176. |
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| Authors: | Li Xinxing Liang Buwen Liu Shiyang Li Hui |
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| Institution: | 1.College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 2. Key Laboratory of Agricultural Informatization Standardization, Ministry of Agriculture and Rural Affairs, Beijing, China |
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| Abstract: | Abstract: Apples are the most widely consumed fruits in dominant agricultural products with the largest planting area in China. However, mechanized pruning of apple trees is still lacking, particularly on the automation and intelligence, due to a high intensity of manual labor in current China. Artificial intelligence techniques for pruning can be expected to greatly improve the photosynthetic efficiency of fruits, thereby to optimize the transport and distribution of nutrients in fruit trees for the better yield and quality. In this study, an intelligent decision support system for pruning apple tree was established using the local point cloud and BP neural network, according to tree shape and artificial intelligent decision making framework. The first fruiting fusiform apple trees were selected as experimental subjects, in order to obtain the actual conditions of tree branches in an apple. The data collection site was selected as the Bakou Fruit Tree Experimental Garden in Beijing, China, and the collection time was autumn 2019. A MESA SR4000 Time of Flight (ToF) depth camera was selected as the data acquisition device. 15 fruit trees with regular shapes were set for data collection, 8 different angles for each tree from which to capture the branch part, and finally a total of 120 datasets were collected. 5 feature values were used to represent the spatial morphology of apple tree branches, where the local point cloud segmentation technique was selected for the 3D skeleton extraction of branches. Specifically, a Harris corner-point detection was conducted to generate the initial candidate points, then to filter the neighboring candidate points by cohesive hierarchy, and thereby extract the 3D skeletal key points by depth hierarchy analysis further to establish the spatial vectors of branches using the line coverage, as well as obtain the 3D spatial morphological feature data of apple tree branches. Thus, a 3D skeletal map of branches was achieved to realize the digital simulation of the real tree branches. A BP neural network using pruning decision was then established to discriminate the competing branches and disturbing branch types (dorsal, centripetal and competing branches) with 3D skeletal maps, while determine whether the branch was cut or not. The learning rate of the system reached 0.85 after training and testing, when the implicit layer node was 10. The pruning decision scheme has a good degree of discrimination for dorsal and centripetal branches, with an accuracy rate of more than 90%. The overall detection rate of interference branches was 85.71%, indicating a high standard level. Taking Python as the programming language, while the Django and TensorFlow as the deep learning framework, a new system was developed with the functions of 3D bone extraction of tree branches, pruning apple tree, and information query of apple tree pruning. The system can realize the digital processing and intelligent pruning of apple trees, providing scientific and reliable tools for mechanized pruning of fruit trees. |
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| Keywords: | intelligent systems agricultural engineering apple pruning three-dimensional point clouds BP neural network |
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