Comparing Unsupervised and Supervised Classifiers on Multispectral UAV Data to Detect Crop Water-Nitrogen Co-Limitation

Abstract

The high spatio-temporal resolution of UAV sensors requires robust analytical tools to classify subtle agroecosystem variations. This study compared unsupervised, supervised machine learning (ML), and deep learning (U‑Net) classifiers to identify nitrogen (N) and water (I) status, and their interaction (N×I) in potato crops using UAV multispectral imagery. The U‑Net model outperformed all other methods, achieving accuracies of 85% (N), 93% (I), and 70% (N×I). Supervised ML classifiers also performed well and Support Vector Machine achieved 71, 62, and 40% respectively, whereas Random Forest achieved 67, 61, and 40%. The unsupervised K‑means classifier yielded the lowest accuracies (41, 36, and 23%), demonstrating the necessity of substantial supervision to delineate crop N and water properties. These results were confirmed by repeated analysis on UAV imagery acquired later in the season. Deep learning classifiers should be adopted more widely in precision agriculture, as they offer new potential for optimizing N and irrigation co-management under field conditions with subtle spatial variation that is otherwise difficult to capture. Future research should test alternative deep learning algorithms and sensor data fusion to further improve classification accuracies.

Plant phenotyping relevance

UAVマルチスペクトル画像からジャガイモの窒素・水分状態を推定し、複数の分類器を比較・反復検証しており、植物状態の取得・推定手法が研究の中心である。

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