# Enhancing Aboveground Biomass Prediction through Integration of the SCDR Paradigm into the U-Like Hierarchical Residual Fusion Model

## TL;DR

This paper introduces a new deep learning model for predicting plant biomass using improved contrastive learning and a specialized network, achieving better accuracy on image-based datasets.

## Contribution

The novel Improved Supervised Contrastive Deep Regression (SCDR) and U-like Hierarchical Residual Fusion Network (BioUMixer) enhance biomass prediction by capturing complex feature relationships.

## Key findings

- The BioUMixer model achieved RMSE = 252.18, MAE = 201.98, and MAPE = 0.107 on the Pepper_Biomass dataset.
- On the GrassClover dataset, the model achieved RMSE = 47.92, MAE = 31.74, and MAPE = 0.192.
- The proposed methods improve biomass prediction accuracy by integrating global and local image features.

## Abstract

Deep learning methodologies employed for biomass prediction often neglect the intricate relationships between labels and samples, resulting in suboptimal predictive performance. This paper introduces an advanced supervised contrastive learning technique, termed Improved Supervised Contrastive Deep Regression (SCDR), which is adept at effectively capturing the nuanced relationships between samples and labels in the feature space, thereby mitigating this limitation. Simultaneously, we propose the U-like Hierarchical Residual Fusion Network (BioUMixer), a bespoke biomass prediction network tailored for image data. BioUMixer enhances feature extraction from biomass image data, facilitating information exchange and fusion while considering both global and local features within the images. The efficacy of the proposed method is validated on the Pepper_Biomass dataset, which encompasses over 600 original images paired with corresponding biomass labels. The results demonstrate a noteworthy enhancement in deep regression tasks, as evidenced by performance metrics on the Pepper_Biomass dataset, including RMSE = 252.18, MAE = 201.98, and MAPE = 0.107. Additionally, assessment on the publicly accessible GrassClover dataset yields metrics of RMSE = 47.92, MAE = 31.74, and MAPE = 0.192. This study not only introduces a novel approach but also provides compelling empirical evidence supporting the digitization and precision improvement of agricultural technology. The research outcomes align closely with the identified problem and research statement, underscoring the significance of the proposed methodologies in advancing the field of biomass prediction through state-of-the-art deep learning techniques.