Detection and Classification of Different Leaf Diseases in Solanum torvum using Deep Learning

Fungal disease is a serious threat for plant life. Detection of this fungal disease under laboratory is a time consuming and laborious work. Image processing techniques can be used for early detect of these diseases. This paper discusses the detection of Early Blight and Late Blight diseases in Solanum torvum species. Early Blight is caused by Alterneria Solani and Late Blight is caused by Phythphora Infestens. Both fungal pathogens are identified in the laboratory of department of Botany, Madhabdev University, Assam. After identification of fungal disease image processing is carried out with the help of Convolution Neural Network. In this study, by using the EfficientNetB0 model CNN architecture, initially 98.75% accuracy has been observed and after fine-tuning 100% of classification accuracy has been achieved.

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Publication Details

Journal
International Journal of Environmental Sciences
Published
2025-09-19
DOI
https://doi.org/10.64252/z7618425
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
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article

Detection and Classification of Different Leaf Diseases in Solanum torvum using Deep Learning

Lairenjam Obiroy Singh, Ritaban Dutta, Mutum Bıdyaranı Devi, Gautam Saikia
International Journal of Environmental Sciences
Smart Agriculture and AI
article

Detection and Classification of Different Leaf Diseases in Solanum torvum using Deep Learning

Lairenjam Obiroy Singh, Ritaban Dutta, Mutum Bıdyaranı Devi, Gautam Saikia
article en

Abstract

Fungal disease is a serious threat for plant life. Detection of this fungal disease under laboratory is a time consuming and laborious work. Image processing techniques can be used for early detect of these diseases. This paper discusses the detection of Early Blight and Late Blight diseases in Solanum torvum species. Early Blight is caused by Alterneria Solani and Late Blight is caused by Phythphora Infestens. Both fungal pathogens are identified in the laboratory of department of Botany, Madhabdev University, Assam. After identification of fungal disease image processing is carried out with the help of Convolution Neural Network. In this study, by using the EfficientNetB0 model CNN architecture, initially 98.75% accuracy has been observed and after fine-tuning 100% of classification accuracy has been achieved.

International Journal of Environmental Sciences
Openalex Percentile: Top 79%
Smart Agriculture and AI
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