Abiotic Stress Prediction from RGB-T Images of Banana Plantlets
This work provides a method for early and accurate abiotic stress detection in banana plantlets, which is significant for agricultural monitoring and yield optimization.
This paper addresses the problem of abiotic stress prediction in banana plantlets using RGB and thermal (RGB-T) images. The authors developed neural network models that achieved over 90% prediction accuracy across four distinct water and fertilizer treatments, outperforming human experts.
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.