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基于IM-SSD+ACO算法的整株大豆表型信息提取
引用本文:宁姗,陈海涛,赵秋多,王业成. 基于IM-SSD+ACO算法的整株大豆表型信息提取[J]. 农业机械学报, 2021, 52(12): 182-190
作者姓名:宁姗  陈海涛  赵秋多  王业成
作者单位:东北农业大学工程学院,哈尔滨150030;黑龙江科技大学工程训练与基础实验中心,哈尔滨150022;东北农业大学工程学院,哈尔滨150030
基金项目:国家重点研发计划项目(2016YFD0100201、2018YFD0201004)
摘    要:为了减少检测整株大豆豆荚及茎秆时相互遮挡对精度造成的影响,提出了一种基于卷积神经网络的大豆豆荚及茎秆表型信息检测方法,根据大豆植株的生长特征和卷积网络的特点,对单次多框检测器(Single shot muhibox detector,SSD)进行了改进.与传统SSD相比,改进SSD(IM-SSD)具有更好的抗干扰能力和...

关 键 词:大豆植株  目标检测  卷积神经网络  蚁群优化
收稿时间:2020-12-08

Detection of Pods and Stems in Soybean Based on IM-SSD+ACO Algorithm
NING Shan,CHEN Haitao,ZHAO Qiuduo,WANG Yecheng. Detection of Pods and Stems in Soybean Based on IM-SSD+ACO Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(12): 182-190
Authors:NING Shan  CHEN Haitao  ZHAO Qiuduo  WANG Yecheng
Affiliation:Northeast Agricultural University
Abstract:Soybean is an important crop in agriculture and plays an important role in the agricultural field of the world. With the increase of population and disasters caused by abnormal climate, how to cultivate more adaptive high-yield crop varieties has become a major problem faced by breeding experts. A soybean detection method was proposed based on deep learning to reduce the influence of illumination, growth difference and occlusion. In view of characteristics of soybean and accuracy of deep learning, single shot multibox detector (SSD) was improved. Compared with the SSD, the improved SSD (IM-SSD) had better anti-interference ability and self-learning ability. The first step was to build datasets by taking 3695 photos of harvested soybean plants under the fixed and defined light condition and blue background described. And the training set was randomly changed by images translation, rotation and scaling to expand the datasets. Then training datasets were used to train and construct network model. To study the effect of base network and the input size of networks, one contrast experiment was designed on SSD and IM-SSD. Through the analysis of the experimental results,the average accuracy of IM-SSD was 7.79 percentage points higher than that of SSD300 and 3.83 percentage points higher than that of SSD512, respectively. Compared with SSD, IM-SSD was improved in soybean pod and stem detection. Nevertheless, the location of the stem by IM-SSD was discontinuous. A method of stem extraction was proposed, which used IM-SSD results and ant colony optimization (ACO) algorithm to extract the whole stem. The experimental results showed that the IM-SSD and the stem extraction method could accurately locate pod and stem of soybean plants. Finally, some phenotypic information of soybean plants was obtained, including the number of pods of the whole plant, plant height, effective branch number, main stem and plant type.
Keywords:soybean plant   target recognition   convolution neural network   ant colony optimization
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