An algorithm for automatic identification of multiple developmental stages of rice spikes based on improved Faster R-CNN  

作　　者：Yuanqin Zhang Deqin Xiao Youfu Liu Huilin Wu 

机构地区：[1]College of Mathematics and Informatics,South China Agricultural University,Guangzhou 510642,Guangdong,China [2]Guangzhou National Modern Agricultural Industry Science and Technology Innovation Center,Guangzhou 511458,Guangdong,China

出　　处：《The Crop Journal》2022年第5期1323-1333,共11页作物学报（英文版）

基　　金：supported by the Key-Area Research and Development Program of Guangdong Province (2019B020214005);Agricultural Research Project and Agricultural Technology Promotion Project of Guangdong (2021KJ383)。

摘　　要：Spike development directly affects the yield and quality of rice. We describe an algorithm for automatically identifying multiple developmental stages of rice spikes(AI-MDSRS) that transforms the automatic identification of multiple developmental stages of rice spikes into the detection of rice spikes of diverse maturity levels. The scales vary greatly in different growth and development stages because rice spikes are dense and small, posing challenges for their effective and accurate detection. We describe a rice spike detection model based on an improved faster regions with convolutional neural network(Faster R-CNN).The model incorporates the following optimization strategies: first, Inception_Res Net-v2 replaces VGG16 as a feature extraction network;second, a feature pyramid network(FPN) replaces single-scale feature maps to fuse with region proposal network(RPN);third, region of interest(Ro I) alignment replaces Ro I pooling, and distance-intersection over union(DIo U) is used as a standard for non-maximum suppression(NMS). The performance of the proposed model was compared with that of the original Faster R-CNN and YOLOv4 models. The mean average precision(m AP) of the rice spike detection model was92.47%, a substantial improvement on the original Faster R-CNN model(with 40.96% m AP) and 3.4%higher than that of the YOLOv4 model, experimentally indicating that the model is more accurate and reliable. The identification results of the model for the heading–flowering, milky maturity, and full maturity stages were within two days of the results of manual observation, fully meeting the needs of agricultural activities.

关 键 词：Improved Faster R-CNN Rice spike detection Rice spike count Developmental stage identification 

分 类 号：S511[农业科学—作物学] TP183[自动化与计算机技术—控制理论与控制工程]