DeepLeaf: Automated Plant Disease Diagnosis using Deep Learning Approach

S.Sailaja; E.V.N.Jyothi; M.Kranthi

doi:10.58599/IJSMCSE.2024.1107

Authors

S.Sailaja Department of Computer Science and Engineering, RISE Krishna Sai Prakasam Group of Institutions, Ongole, Andhra Pradesh, India
E.V.N.Jyothi Department of Computer Science and Engineering, RISE Krishna Sai Prakasam Group of Institutions, Ongole, Andhra Pradesh, India
M.Kranthi Department of Computer Science and Engineering, RISE Krishna Sai Prakasam Group of Institutions, Ongole, Andhra Pradesh, India

DOI:

https://doi.org/10.58599/IJSMCSE.2024.1107

Keywords:

Plant disease diagnosis, Deep learning, Convolutional neural networks, Agriculture, Automated diagnosis

Abstract

Automated diagnosis of plant diseases is critical for ensuring food security and agricultural sustainability. In this study, we propose DeepLeaf, a novel deep-learning framework for the automated recognition and classification of plant diseases. DeepLeaf leverages convolutional neural networks to analyze plant leaf images and accurately identify disease symptoms. The framework is trained on a large dataset of annotated images, encompassing a wide range of plant species and disease types. Through extensive experimentation, we demonstrate the effectiveness of DeepLeaf in accurately diagnosing plant diseases across diverse environmental conditions and varying degrees of disease severity. Our results show that DeepLeaf achieves high accuracy and robustness, outperforming traditional methods and commercial systems in speed and reliability. Furthermore, DeepLeaf is designed to be easily deployable in real-world agricultural settings, enabling farmers and agronomists to identify and mitigate plant diseases quickly, thus refining crop yield and dropping economic losses.

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