Enhancing grain moisture prediction in multiple crop seasons using domain adaptation AI

Abstract

• Developing a domain adaptation (DA) workflow for predicting rice grain moisture content (GMC) in multiple crop seasons. • Designing a real-time light-weight detector using U 2 -Net and YOLOv7 with a prediction error less than 1 % GMC. • Achieving a 54.66 % improvement in DA-modified GMC prediction with only 0.1 % target domain data. • Generating a rice maturity map through supervised and unsupervised DA with prediction errors within 1.2 % GMC. In precision agriculture, the real-world performance of image-based artificial intelligence models is often adversely affected by variations in, for example, illumination, spectral reflectance, species, growth stages, and crop seasons between the target and source domains. In this study, a machine learning domain adaptation (DA) workflow was developed to quantify the grain moisture content (GMC) of rice and enhance model robustness across crop seasons and conditions. A GMC data set was established using imagery from mobile devices and GMC survey data covering 9 growing seasons (2019–2023), 38 species, and 2 background conditions. U 2 -Net and YOLOv7 models were used to preprocess the imagery and extract and correct panicles’ geometric and spectral features. A total of 11 machine learning models and 22 DA algorithms were evaluated to identify the optimal model for GMC prediction. Experiments with supervised and unsupervised DA were conducted using feature-, instance-, and parameter-based algorithms. The instance-based algorithms achieved the lowest mean absolute error (MAE), 0.28 %. The target domain data revealed that supervised DA at a 0.1 % data ratio has moderated high performance, with a MAE of 1.51 %. When the GMC was below 40 %, the MAE was 1.07 %, indicating a 54.66 % improvement over models without DA, with an error of ±1 day. These results were visualized over a 900-ha rice field, demonstrating the models’ robustness, with GMC interval differences within 1.2 %. This DA workflow can enhance model adaptability in agriculture, indicating it holds potential in precision agriculture.

Topics & Concepts