Cotton top bud recognition method based on YOLOv5-CPP in complex environment

Cotton bud is one of the most important cash crops with many uses, such as the textile and cotton fabric raw material. However, manual cotton topping cannot fully meet the large-scale production in recent years, due to the efficiency and labor costs. Intelligent mechanical topping can be expected to serve as an inevitable trend during cotton topping. There is a high demand to accurately identify the cotton top bud under the complex environment (such as light and shadow), particularly for the various shape characteristics of cotton top core in the process of cotton cap removal. In this study the accurate recognition was proposed to locate the cotton top bud in the complex environment using an improved YOLOv5s. A dataset was also constructed to contain 3103 cotton top buds with different morphological characteristics under a complex environment. Three categories were divided: single, multi-plant and screened top bud under different occlusion areas, weather and light conditions. Systematic training and analysis were then performed on the data. An improved YOLOv5s model was proposed to reduce the error and leakage detection for the sufficient features of small targets. Multi-scale target features were also effectively extracted using only three detection heads in the original YOLOv5s. At the same time, the ideal extraction of the improved YOLOv5s was obtained with the deepening of the network layer. The object detection and CPP-CBAM attention mechanism were first added to the original structure, in order to improve the shallow and deep feature fusion rate. Secondly, the regression of prediction frame was enhanced to avoid the information loss of the real frame. The loss function SIOU boundary frame regression was added to further accelerate the prediction frame regression speed for the detection accuracy. The SIOU loss function was introduced by the vector angle between the real and predicted frame including angle, distance, and shape loss function. As such, the aspect ratio between the predicted and real frame was considered in the CIOU loss function with the direction between them in the SIOU. The high accuracy of recognition rate was obtained on the top bud, resulting in the less degrading into an IOU loss function. In addition, an accurate identification was performed on the cotton top bud that was seriously obscured by the leaves. The results show that the average accuracy (mAP) reached 92.8% for the recognition of cotton top bud by the improved model. Furthermore, the average accuracy was improved by 2.1, 3.3, 2, and 2.4 percentage points, respectively, compared with Fast R-CNN, YOLOv3, YOLOv5s, and YOLOv6. The improved detection model can be expected to accurately and efficiently identify the cotton top bud under a complex environment in real time. The small size of the model parameters and the high identification speed were suitable for the migration deployment of cotton topping machinery, and the operation of the laser topping robot. The finding can also provide technical and theoretical support to the subsequent operation of cotton laser topping machines, particularly for the mechanized real-time topping.