Unmanned aerial system and machine learning driven Digital-Twin framework for in-season cotton growth forecasting

Abstract

In the past decade, Unmanned Aerial Systems (UAS) have made a significant impact on various sectors, including precision agriculture, by enabling remote monitoring of crop growth and development. Monitoring and managing crops effectively throughout the growing season are crucial for optimizing crop yield. The integration of UAS-monitored data and machine learning has greatly advanced crop production management, resulting in improvements in key areas such as irrigation scheduling, crop termination analysis, and predicting yield. This study presents the development of a Digital Twin (DT) for cotton crops using UAS captured RGB data. The primary objective of this DT is to forecast various cotton crop features during the growing season, including Canopy Cover (CC), Canopy Height (CH), Canopy Volume (CV), and Excess Greenness (EXG). Predictive analytics as part of DT development employs machine learning regression to extract crop feature growth patterns from UAS data collected from 2020 to 2023. During the current season, real-time UAS data and historical growth patterns are combined to generate growth patterns using a novel hybrid model generation strategy for forecasting. Comparisons of the DT-based forecasts to actual data demonstrated low RMSE for CC, CH, CV, and EXG. The proposed DT framework, which accurately forecasts cotton crop features up to 30 days into the future starting 80 days after sowing, was found to outperform existing forecasting methods. Notably, the RRMSE for CC, CH, CV, and EXG was measured to be 9, 13, 14, and 18 percent, respectively. Furthermore, the potential applications of forecasted data in biomass estimation and yield prediction are highlighted, emphasizing their significance in optimizing agricultural practices.

Plant phenotyping relevance

UAS画像と機械学習を統合したDigital Twinによる綿花のキャノピー形質（被覆、草高、体積、緑度）の抽出・将来予測が研究の中心であり、実測値との比較検証も行っている。

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