Robotic Multimodal Data Acquisition for In-Field Deep Learning Estimation of Cover Crop Biomass

TL;DR

This paper presents a robotic system that combines optical and LiDAR sensors with machine learning to accurately estimate cover crop biomass in fields, aiding targeted weed management and sustainable agriculture.

Contribution

It introduces a novel multimodal sensor system mounted on a ground robot and applies ML data fusion for improved biomass estimation in agricultural settings.

Findings

Achieved a coefficient of determination of 0.88 for dry AGB prediction.

Demonstrated robust performance across diverse field conditions.

Enabled precise, site-specific weed suppression strategies.

Abstract

Accurate weed management is essential for mitigating significant crop yield losses, necessitating effective weed suppression strategies in agricultural systems. Integrating cover crops (CC) offers multiple benefits, including soil erosion reduction, weed suppression, decreased nitrogen requirements, and enhanced carbon sequestration, all of which are closely tied to the aboveground biomass (AGB) they produce. However, biomass production varies significantly due to microsite variability, making accurate estimation and mapping essential for identifying zones of poor weed suppression and optimizing targeted management strategies. To address this challenge, developing a comprehensive CC map, including its AGB distribution, will enable informed decision-making regarding weed control methods and optimal application rates. Manual visual inspection is impractical and labor-intensive, especially given the extensive field size and the wide diversity and variation of weed species and sizes. In this context, optical imagery and Light Detection and Ranging (LiDAR) data are two prominent sources with unique characteristics that enhance AGB estimation. This study introduces a ground robot-mounted multimodal sensor system designed for agricultural field mapping. The system integrates optical and LiDAR data, leveraging machine learning (ML) methods for data fusion to improve biomass predictions. The best ML-based model for dry AGB estimation achieved a coefficient of determination value of 0.88, demonstrating robust performance in diverse field conditions. This approach offers valuable insights for site-specific management, enabling precise weed suppression strategies and promoting sustainable farming practices.