Early Plant Stress Detection using Thermal Leaf Patterns with TAP-EfficientNet for Precision Agriculture
DOI:
https://doi.org/10.15662/IJEETR.2026.0802020Keywords:
Precision, Agriculture, Early Plant Stress Detection, Thermal Imaging, TAP-EfficientNet, Thermal Anomaly Pattern (TAP), Convolutional Neural Networks (CNNs) Spatial Attention, Edge DevicesAbstract
The objective of this study is to employ the proposed TAP-EfficientNet model for the early detection of plant stress using thermal leaf patterns, aiming to improve diagnostic accuracy and computational efficiency in precision agriculture. Group 1 is the standard EfficientNet baseline model. Group 2 is the proposed TAP-EfficientNet model. A sample size of 500 thermal leaf images is used for each group, and data is collected across various time intervals and stress conditions (e.g., water deficit, disease). The models' classification accuracy, precision, recall, F1-score, and inference delay are all calculated. The output demonstrated that the TAP-EfficientNet model has better classification results than the standard EfficientNet model in terms of 5.4% higher accuracy, 4.8% higher precision, 6.2% higher F1-score, and [e.g., 12.5%] lower inference delay. The results of the experiment indicate that the suggested TAP-EfficientNet model can detect early plant stress more effectively than the standard EfficientNet model, making it highly suitable for real-time monitoring and deployment in precision agriculture.
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