Article
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Preserved in Portico This version is not peer-reviewed
Entropy as the High-Level Feature for XAI-Based Early Plant Stress Detection
Version 1
: Received: 19 August 2022 / Approved: 22 August 2022 / Online: 22 August 2022 (15:47:02 CEST)
A peer-reviewed article of this Preprint also exists.
Lysov, M.; Maximova, I.; Vasiliev, E.; Getmanskaya, A.; Turlapov, V. Entropy as a High-Level Feature for XAI-Based Early Plant Stress Detection. Entropy 2022, 24, 1597. Lysov, M.; Maximova, I.; Vasiliev, E.; Getmanskaya, A.; Turlapov, V. Entropy as a High-Level Feature for XAI-Based Early Plant Stress Detection. Entropy 2022, 24, 1597.
Abstract
The article is devoted to solving the problem of searching for universal explainable features that can remain explainable for a wide class of objects and phenomena and become an integral part of Explainable AI (XAI). The study is implemented on the example of an applied problem of early diagnostics of plant stress, using Thermal IR (TIR) and HSI, presented by 8 vegetation indices/channels. Each such index was presented by 5 statistical values. A Single-Layer-Perceptron classifier was used as the main instrument. TIR turned out to be the best of the indices in terms of efficiency in the field and sufficient to detect all 7 key days with 100% accuracy. Our study shows also that there are a number of indices, inluding NDVI, and usual color channels Red, Green, Blue, which are close to TIR possibilities in early plant stress detection with 100% accurasy or near, and can be used for wide class of plants and in different conditions their treatment. The stability of the stress classification in our study was maintained when the training set was reduced up to 10% of the dataset volume. The entropy-like feature of (max-min) for any indices/channels have determined as a leadersheep universal high-level explainable feature for the plant stress detection, which used in interaction with some of other statistical features.
Keywords
explainable artificial intelligence; high-level explainable feature; entropy; plant stress; early diagnosis
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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