Abstract
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_ResNet-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 (RoI) alignment replaces RoI pooling, and distance-intersection over union (DIoU) 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 (mAP) of the rice spike detection model was 92.47%, a substantial improvement on the original Faster R-CNN model (with 40.96% mAP) 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.
