UAV-based monitoring of fruit-manifested abiotic stress in crops under label scarcity: a case study of blossom-end rot in processing tomatoes.

Abstract

Introduction Safeguarding the yield and quality of field crops against abiotic stresses is critical for large-scale agricultural production and precision agronomy. This study addresses the challenges of detecting concealed fruit stress and overcoming label scarcity in unmanned aerial vehicle (UAV) multispectral monitoring, using blossom-end rot (BER) in processing tomatoes as a case study. Methods We developed a leaf-fruit synergistic Roll and Color Disease Index (RCDI), integrating fruit incidence with visible canopy phenotypic features to characterize the combined canopy-fruit stress status associated with BER. A three-level screening strategy was used to identify the optimal spectral feature set. A Multi-model Collaborative Cyclic Self-Training (MCC-ST) framework was subsequently developed to address the limited availability of severity-labeled samples. Results The combination of GRVI, NDVI, and SAVI was identified as the optimal spectral feature set, achieving stable within-dataset binary classification performance of approximately 97% in repeated cross-validation. Under 30 random-seed repeated stratified three-fold cross-validations, MCC-ST + DT and MCC-ST + RF achieved RCDI-based BER severity-grading accuracies of 85.00% ± 0.31% and 85.07% ± 0.42%, respectively. Compared with the corresponding original DT and RF models, MCC-ST improved repeated-validation accuracy by 16.19-4.09 percentage points. Discussion The RCDI helps bridge the observational gap between canopy signals and concealed fruit stress, while MCC-ST alleviates the bottleneck associated with label scarcity. The proposed approach provides a promising framework for crop abiotic-stress monitoring under limited-label conditions.

Plant phenotyping relevance

UAVマルチスペクトル画像からトマトの果実ストレス状態・BER重症度を推定する指標と、ラベル不足に対応する機械学習フレームワークを開発・検証しており、植物表現型取得・推定手法が研究の中心である。

Code and data availability

The paper reports UAV multispectral imagery, ground-truth RCDI/DI measurements, and MCC-ST model results, but provides no public repository deposit or author URL for any dataset or code. The data availability statement only promises raw data from the authors on request; the supplementary-material link is generic and no

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