# Clover Dry Matter Predictor Based on Semantic Segmentation Network and Random Forest
###### Abstract
[16] developed a process-based model \"NORNE\" to predict the dry matter yield of grassland using remote sensing information from a drone, while [PERSON] et al. [17] used a near-infrared (NIR) imaging system to identify red clover and estimate the weight. Neural networks have achieved remarkable success in domains such as computer vision and related fields [18; 19; 20]. The shortcoming of using them for the task of clover dry matter prediction is that labeling the dry matter content of each pixel, attributable to the possible presence of noise, occlusion and illumination variations in the image, generates a random error that affects the prediction performance. This study is carried out through the amalgamation of the potent feature extraction prowess inherent in semantic segmentation networks [21; 22; 23; 24] and the excellent regression performance of RF, and by borrowing the concept of transfer learning [25; 26; 27]. In this research endeavor, we introduce a predictive methodology that hinges on the synergy between semantic segmentation networks and RF algorithms, which is able to efficiently analyze the dry matter prediction of clover images at the pixel level. DeepLabv3+, an advanced semantic segmentation network with excellent pixel-level feature extraction and image segmentation capabilities, is first used. By associating each pixel in the image with a specific semantic label, it was possible to accurately distinguish clover, background and other elements. This provides a strong basis for subsequent dry matter content prediction. Further, RF regression is introduced as a complement to further dry matter prediction in clover images. RF is an integrated learning method, which consists of multiple decision trees and can effectively handle complex nonlinear relationships. By combining the feature representation of the semantic segmentation network and the regression capability of RF, the dry matter content of each pixel in clover images can be predicted more accurately. The main contributions and innovations of this paper are as follows:  
* It obtains the rich feature representation of clover images through semantic segmentation network, and then combines it with RF regression to construct a dry matter prediction model of clover images to realize the function of predictive analysis of dry matter content. * It uses the DeepLabv3+ network with MobileNetv2 as the backbone as the feature extraction network and uses the SE attention mechanism to improve the ASPP, which, compared with the FCN-8s model used by the producer of the open-source dataset, has an improvement of up to 18% in the mIoU collinearity for the semantic segmentation task of the GressClovers dataset. has an improvement of up to 18.5%. * It obtains the pixel-level features of the species in the image through semantic segmentation to understand the semantic information of the various classes in the image, which is used to obtain the deep information linking the distribution of the species in the image to the dry matter by constructing a RF regression model. * Provides a new, low-cost and efficient solution for the prediction of dry matter in clover. The remainder of this paper is organized into three sections. The second section will detail the model used in this paper while explaining the methodology and content within the model. In Section 3, we delve into the specifics of the experimental design. Finally, Section 4 presents a comprehensive discussion of the experimental results and offers insights into the future prospects of the application.