Clover Dry Matter Predictor Based on Semantic Segmentation Network and Random Forest

Abstract

As a key animal feed source, the dry matter content of clover is widely regarded as an important indicator of its nutritional value and quality. The primary aim of this study is to introduce a methodology for forecasting clover dry matter content utilizing a semantic segmentation network. This approach involves constructing a predictive model based on visual image information to analyze the dry matter content within clover. Given the complex features embedded in clover images and the difficulty of obtaining labeled data, it becomes challenging to analyze the dry matter content directly from the images. In order to address this issue, a method for predicting dry matter in clover based on semantic segmentation network is proposed. The method uses the improved DeepLabv3+ network as the backbone of feature extraction, and integrates the SE (Squeeze-and-Excitation) attention mechanism into the ASPP (Atrous Spatial Pyramid Pooling) module to enhance the semantic segmentation performance, in order to realize the efficient extraction of the features of clover images; on this basis, a regression model based on the Random Forest (RF) method is constructed to realize the prediction of dry matter in clover. Extensive experiments conducted by applying the trained model to the dry matter prediction dataset evaluated the good predictor performance and showed that the number of each pixel level after semantic segmentation improved the performance of semantic segmentation by 18.5% compared to the baseline, and there was a great improvement in the collinearity of dry matter prediction.