Identification of Cotton Growing Stage Based on Faster-RCNN

With computer vision booming ahead on the high-way, image classification and object detection have greatly improved, which are able to accurately classify large datasets even better than human beings. There are many fields in which computer vision gets involved, including terrestrial and aerial mapping of natural resources, crop monitoring, precision agriculture, quality control and classification of processing lines, and process automation. The application of computer vision in agriculture has increased considerably in recent years. However, traditional computer vision methods require complex preprocessing and algorithms, which is time-consuming and labor-intensive. In particular, the effectiveness of this method relies mainly on the accuracy of the artificial design features and the learning algorithm. In this paper, we propose a object detection algorithm based on Faster-RCNN to identify the stage of the cotton leaf(a period that cotton leaves grow). The algorithm consists of three main parts: convolutional neural network based on ResNet101, the extraction of candidate regions of the target based on RPNs, and the identification of detection object. Based on many experiments, the algorithm in this paper is able to identify the cotton leaf stage, and the precision surpasses 80%.