Evolutionary Computing Driven ROI-Specific Spatio-Temporal Statistical Feature Learning Model for Medicinal Plant Disease Detection and Classification

Evolutionary Computing Driven ROI-Specific Spatio-Temporal Statistical Feature Learning Model for Medicinal Plant Disease Detection and Classification

© 2022 by IJETT Journal
Volume-70 Issue-6
Year of Publication : 2022
Authors : Margesh Keskar, Dhananjay D Maktedar
DOI : 10.14445/22315381/IJETT-V70I6P220

How to Cite?

Margesh Keskar, Dhananjay D Maktedar, "Evolutionary Computing Driven ROI-Specific Spatio-Temporal Statistical Feature Learning Model for Medicinal Plant Disease Detection and Classification," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 165-184, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P220

Plants can be hypothesized to be the inevitable need of living beings on earth. Amongst the gigantically large plant species and varieties, the medicinal plants have a distinct and significant role in herbal remedies, ayurvedic medicine, the pharmaceutical industry, and the major modern medicine world. Various medicinal plants like roots, stems, and leaves are used for the abovementioned purposes; however, their efficacy depends on their intrinsic health condition. In other words, a medicinal plant with healthy and non-contentious characteristics can positively impact medicinal uses. On the contrary, plants with the disease can have a negative or insignificant impact on medicinal purposes. In sync with this fact, detecting plant disease over the different medicinal plants can be vital for healthy plant selection and identifying diseases for preventive measures or decisions. Despite the robustness of the vision-based automatic plant disease detection and classification systems, the non-uniform disease patterns, non-ROI feature learning, and inferior feature space confine the efficacy of the major athand solutions. To alleviate such limitations, in this paper, a highly robust evolutionary computing-driven ROI-specific Spatio-temporal statistical feature learning model is developed for medicinal plant disease detection and classification. To ensure solution optimality, the proposed model first performed pre-processing employing image histogram equalization, intensity equalization, and Z-score normalization, followed by annotations. Subsequently, a first-of-its-kind Firefly algorithm-driven Fuzzy C-Means clustering (FFCM) was developed for ROI segmentation. Subsequently, the proposed model performed an ROI-specific color space overlay to reconstruct ROI in RGB color space to extract significant Spatio-temporal statistical or textural features. In the proposed model, eight Gray-level co-occurrence metrics named correlation, heterogeneity, entropy, energy, contrast, mean, standard deviation, and variance were extracted as STTF features, which were subsequently applied to perform two-class classification for healthy and diseased medicinal plant classification. The simulated results revealed that the proposed model yields superior medicinal plant disease detection and classification performance in terms of accuracy (98.62%), precision (98.81%), recall (98.79%), and F-Measure (0.988).

GLCM Features Heuristic-based ROI Segmentation, Medicinal Plant Disease Detection, Neuro-computing.

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