Entropy as a High-Level Feature for XAI-Based Early Plant Stress Detection

Lysov, Maxim; Maximova, Irina; Vasiliev, Evgeny; Getmanskaya, Alexandra; Турлапов, Вадим

doi:10.3390/e24111597

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…In the examples of providing explainability in the practice of diagnosing plant stress based on DL [2,3], we can speak so far only of partial implementation of three principles, (1) Explanation, (2) Meaningful, and (3) Accuracy of explanation, in order to overcome some of the limitations of the black-box nature of DL models. In examples of providing explainablity based on ML methods [6,13,26], one can see the implementation of the same 3 XAI principles, but with a high degree of content, as well as a part of content of the 4th principle, to which has not yet begun to be given due attention in practice.…”

Section: Notions Of Xai and Explainablity In Aimentioning

confidence: 99%

“…HSI 'explanator' should be centered onto data scientists, and TIR 'explanator' onto biologist and agroscientists. As a result, such XAI block-explanator solves the universal problem of transformation 'explanator' to 'explanator' and can be used at the input of any network for the early diagnosis of plant stress, as example like in [13] or [26].…”

Section: Construction Xai Early Diagnostics Network With Explanators ...mentioning

confidence: 99%

“…To solve this problem was done the following: 1) based on the vegetation index NDblue (1) introduced in [26], a plant mask controlled by a threshold (thr) is constructed; 2) a special tool for cleaning plant masks has been implemented, which changes the mask by limiting the distance to the linear regression from above and below (a,b parameters), and this tool interface (Figure 2) also.…”

Section: Additional Preprocessing Needed To Split the Plant From The ...mentioning

confidence: 99%

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Ensuring Explainability and Dimensionality Reduction in a Multidimensional HSI World for Early XAI-diagnostics of Plant Stress

Lysov¹,

Pukhkiy²,

Vasiliev³

et al. 2023

Preprint

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The work is devoted to the search for effective solutions to the applied problem of early diagnostics of plant stress in the conditions of smart farming and based on modern explicable artificial intelligence (XAI). The study mostly oriented on the theory and practice of XAI, focused on the use of hyperspectral imagery (HSI) and Thermal Infra-Red (TIR) sensor data at the input of a neural network. The first our goal is to build an XAI neural network, explainable due to its structure, the input of which is a datascientist oriented HSI 'explanator', and the output is a biologist oriented TIR 'explanator'. In the middle is SLP-regressor which solves the universal problem of training HSI pixels to temperatures of plants, needed for early plant stress diagnostic. The result can be considered as prototype of a special XAI explanator which is assigned to transform explanator specialized on area 1 onto explanator specialized on area 2. Using this HSI-TIR explanator we ensured the follows: extend HSI data by TIR attribute; providing TIR data for early diagnostic of plant stress; reducing dimensionality HSI needed for TIR training 25 times (from 204 to 8) preserving the same accuracy of temperature prediction (RMSE=0.2-0.3C). This reducing was achieved without using PCA methods. The constructed model is computationally efficient in training: the average training time is significantly less then 1 min (Intel Core i3-8130U, 2.2 GHz, 4 cores, 4 GB). One of the 8 channels, 820 nm, is the leader in correlation with TIR, what allows building local linear temperature prediction functions.

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Section: Notions Of Xai and Explainablity In Aimentioning

confidence: 99%

Section: Construction Xai Early Diagnostics Network With Explanators ...mentioning

confidence: 99%

Section: Additional Preprocessing Needed To Split the Plant From The ...mentioning

confidence: 99%

See 1 more Smart Citation

Ensuring Explainability and Dimensionality Reduction in a Multidimensional HSI World for Early XAI-diagnostics of Plant Stress

Lysov¹,

Pukhkiy²,

Vasiliev³

et al. 2023

Preprint

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Explainable AI for Next Generation Agriculture—Current Scenario and Future Prospects

Dawar,

Negi,

Chauhan

2024

Studies in Computational Intelligence

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Ensuring Explainability and Dimensionality Reduction in a Multidimensional HSI World for Early XAI-Diagnostics of Plant Stress

Lysov

Pukhkiy

Vasiliev

et al. 2023

Entropy

Self Cite

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This work is mostly devoted to the search for effective solutions to the problem of early diagnosis of plant stress (given an example of wheat and its drought stress), which would be based on explainable artificial intelligence (XAI). The main idea is to combine the benefits of two of the most popular agricultural data sources, hyperspectral images (HSI) and thermal infrared images (TIR), in a single XAI model. Our own dataset of a 25-day experiment was used, which was created via both (1) an HSI camera Specim IQ (400–1000 nm, 204, 512 × 512) and (2) a TIR camera Testo 885-2 (320 × 240, res. 0.1 °C). The HSI were a source of the k-dimensional high-level features of plants (k ≤ K, where K is the number of HSI channels) for the learning process. Such combination was implemented as a single-layer perceptron (SLP) regressor, which is the main feature of the XAI model and receives as input an HSI pixel-signature belonging to the plant mask, which then automatically through the mask receives a mark from the TIR. The correlation of HSI channels with the TIR image on the plant’s mask on the days of the experiment was studied. It was established that HSI channel 143 (820 nm) was the most correlated with TIR. The problem of training the HSI signatures of plants with their corresponding temperature value via the XAI model was solved. The RMSE of plant temperature prediction is 0.2–0.3 °C, which is acceptable for early diagnostics. Each HSI pixel was represented in training by a number (k) of channels (k ≤ K = 204 in our case). The number of channels used for training was minimized by a factor of 25–30, from 204 to eight or seven, while maintaining the RMSE value. The model is computationally efficient in training; the average training time was much less than one minute (Intel Core i3-8130U, 2.2 GHz, 4 cores, 4 GB). This XAI model can be considered a research-aimed model (R-XAI), which allows the transfer of knowledge about plants from the TIR domain to the HSI domain, with their contrasting onto only a few from hundreds of HSI channels.

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Entropy as a High-Level Feature for XAI-Based Early Plant Stress Detection

Cited by 3 publications

References 29 publications

Ensuring Explainability and Dimensionality Reduction in a Multidimensional HSI World for Early XAI-diagnostics of Plant Stress

Ensuring Explainability and Dimensionality Reduction in a Multidimensional HSI World for Early XAI-diagnostics of Plant Stress

Explainable AI for Next Generation Agriculture—Current Scenario and Future Prospects

Ensuring Explainability and Dimensionality Reduction in a Multidimensional HSI World for Early XAI-Diagnostics of Plant Stress

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