Abiotic Stress Prediction from RGB-T Images of Banana Plantlets

Abstract

Prediction of stress conditions is important for monitoring plant growth stages, disease detection, and assessment of crop yields. Multi-modal data, acquired from a variety of sensors, offers diverse perspectives and is expected to benefit the prediction process. We present several methods and strategies for abiotic stress prediction in banana plantlets, on a dataset acquired during a two and a half weeks period, of plantlets subject to four separate water and fertilizer treatments. The dataset consists of RGB and thermal images, taken once daily of each plant. Results are encouraging, in the sense that neural networks exhibit high prediction rates (over $90\%$ amongst four classes), in cases where there are hardly any noticeable features distinguishing the treatments, much higher than field experts can supply.

Plant phenotyping relevance

RGB・熱画像からバナナ幼植物の非生物的ストレス状態を推定する画像解析手法とデータセットが研究の中心であり、植物状態のフェノタイピングに該当する。

Code and data availability

The paper describes a proprietary RGB-T banana plantlet dataset and custom models, but contains no public deposit, availability statement, or authors' URL for the data, images, annotations, or code. All allowed URLs are affiliations or cited references (Rahan Meristem, Opgal, Keras example), not paper-specific assets.

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