Automated Dehydration Assessment in Cannabis via YOLOv8 Localization and Multi-Modal Feature Classification
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Abstract
Water management is a fundamental aspect of plant physiology, directly influencing photosynthesis and nutrient intake. The objective of this study is to develop and validate a non-invasive, automated pipeline for quantifying water stress in Cannabis sativa L. using computer vision and physiological ground-truth measurements. The study utilized a dataset of 988 original outdoor images, which were expanded to 4,940 images through data augmentation to ensure model robustness. The process integrates the state-of-the-art YOLOv8 model for leaf localization, achieving a mean Average Precision (mAP) of 98.3 percent. A multi-modal feature set was constructed by combining color statistics derived from four color space transformations (RGB, HSV, LAB, and YCrCb) and Gray-Level Co-occurrence Matrix (GLCM) texture descriptors. The features were used to train a classifier to categorize dehydration into six severity levels (Normal, Mild, Moderate, Distinct, Severe, and Extreme), and the classifier was verified against ground-truth leaf water potential (Ψleaf) measurements. The nd- ings show that a Cubic Support Vector Machine (SVM) outperformed Artificial Neural Networks, achieving an overall accuracy of 86.2%. The research provides a scalable, high-precision solution for real-time monitoring in precision agriculture.
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