Design and development of Android based Plant disease detection using Arduino

Prakash, N.; Udayakumar, E.; Kumareshan, N.

doi:10.1109/icsss49621.2020.9202278

Cited by 15 publications

(2 citation statements)

References 9 publications

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“…This represents the most frequently occurring intensity pair in the image, as shown in Equation (12). Higher values of maximum probability indicate a dominant texture pattern within the image sets.…”

Section: Maximum Probabilitymentioning

confidence: 99%

“…Moreover, they have limited success under some circumstances, such as the inability to process natural data in their raw form [5,10]. As a result, deep learning algorithms that automatically extract features as part of their natural operation have been incorporated into research for about a decade [11][12][13]. According to [14], deep learning has emerged as a preferred method for plant disease detection and management due to its increased computing power, storage capabilities, and accessibility of big datasets.…”

Section: Introductionmentioning

confidence: 99%

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Revealing GLCM Metric Variations across a Plant Disease Dataset: A Comprehensive Examination and Future Prospects for Enhanced Deep Learning Applications

Kabir,

Unal,

Akinci

et al. 2024

Electronics

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This study highlights the intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance in the context of plant disease identification. It emphasizes the importance of rigorous dataset evaluation and selection protocols to ensure reliable and generalizable classification outcomes. Through a comprehensive examination of publicly available plant disease datasets, focusing on their performance as measured by GLCM metrics, this research identified dataset_2 (D2), a database of leaf images, as the top performer across all GLCM analyses. These datasets were then utilized to train the DarkNet19 deep learning model, with D2 exhibiting superior performance in both GLCM analysis and DarkNet19 training (achieving about 91% testing accuracy) according to performance metrics such as accuracy, precision, recall, and F1-score. The datasets other than dataset_1 and 2 exhibited significantly low classification performance, particularly in supporting GLCM analysis. The findings underscore the need for transparency and rigor in dataset selection, particularly given the abundance of similar datasets in the literature and the growing trend of utilizing deep learning methods in future scientific research.

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Section: Maximum Probabilitymentioning

confidence: 99%