Recognition of cotton growth period for precise spraying based on convolution neural network

Abstract

Dynamic acquisition of crop morphology is beneficial to real-time variable decision of precise spraying operations in fields. However, the existing spraying quantity regulation has high tolerance on the statistical characteristics of regional morphology, so expensive LiDAR and ultrasonic radar can't make full use of their high accuracy, and can reduce decision speed because of too much detail of branches and leaves. Therefore, designing a novel recognition system embedded machine learning with low-cost monocular vision is more feasible, especially in China, where the agricultural implements are medium sizes and cost-sensitive. In addition, we found that the growth period of crops is an important reference index for guiding spraying. So, taking cotton as a case study, a cotton morphology acquisition by a single camera is established, and a cotton growth period recognition algorithm based on Convolution Neural Network (CNN) is proposed in this paper. Through the optimization process based on confusion matrix and recognition efficiency, an optimized CNN model structure is determined from 9 different model structures, and its reliability was verified by changing training sets and test sets many times based on the idea of k-fold test. The accuracy, precision, recall, F1-score and recognition speed of this CNN model are 93.27%, 95.39%, 94.31%, 94.76% and 71.46 ms per image, respectively. In addition, compared with the performance of VGG16 and AlexNet, the convolution neural network model proposed in this paper has better performance. Finally, in order to verify the reliability of the designed recognition system and the feasibility of the spray decision-making algorithm based on CNN, spraying deposition experiments were carried out with 3 different growth-periods of cotton. The experiments’ results validate that after the optimal spray parameters were applied at different growth periods respectively, the average optimum index in 3 growth periods was 42.29%, which was increased up to 62.24% than the operations without distinguishing growth periods.