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AI-Driven Plant Disease Diagnosis: A Deep Learning Approach to Precision Agriculture
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence, Agriculture, Deep Learnin
Abstract
Plant diseases have long posed a critical challenge to global agricultural sustainability, leading to significant economic losses and food security concerns. Traditional disease detection methods, which rely on manual inspection and laboratory analysis, are often inefficient, time-consuming, and impractical for large-scale farming operations. The integration of artificial intelligence, particularly deep learning, has emerged as a revolutionary approach to overcoming these limitations. Deep learning models can rapidly and accurately identify plant diseases from images, allowing for early intervention and minimizing the spread of infections. This paper explores the application of deep learning techniques in plant disease diagnosis, delving into various models, training methodologies, datasets, real-world applications, and associated challenges. The study highlights the potential of AI-powered disease detection in improving agricultural productivity and sustainability while addressing the technical barriers that need to be overcome for widespread adoption.
Keywords
Artificial Intelligence, Deep Learning, Plant Disease Diagnosis, Precision Agriculture, Convolutional Neural Networks, Machine Learning, Transfer Learning, Image Processing.
References
[1] Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419. https://doi.org/10.3389/fpls.2016.01419
[2] Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep neural networks-based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016, 1-11. https://doi.org/10.1155/2016/3289801
[3] Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311-318. https://doi.org/10.1016/j.compag.2018.01.009
[4] Kumar, V., Rani, R., & Jayaraman, P. P. (2022). AI-driven solutions for real-time plant disease detection and diagnosis. Artificial Intelligence in Agriculture, 6, 78-90. https://doi.org/10.1016/j.aiia.2022.02.003
[5] Xie, X., Ma, Y., Liu, B., He, J., Li, S., & Wang, H. (2020). A deep-learning-based real-time mobile diagnosis system for plant diseases. Frontiers in Plant Science, 11, 590. https://doi.org/10.3389/fpls.2020.00590
How to cite this paper
@article{1708210,
author = {Mansi Bapu Zanje, Sneha Balu Shirke, Ishwari Sanjay Yadav},
title = {AI-Driven Plant Disease Diagnosis: A Deep Learning Approach to Precision Agriculture},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {11},
pages = {690-693},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708210.pdf},
abstract = {Plant diseases have long posed a critical challenge to global agricultural sustainability, leading to significant economic losses and food security concerns. Traditional disease detection methods, which rely on manual inspection and laboratory analysis, are often inefficient, time-consuming, and impractical for large-scale farming operations. The integration of artificial intelligence, particularly deep learning, has emerged as a revolutionary approach to overcoming these limitations. Deep learning models can rapidly and accurately identify plant diseases from images, allowing for early intervention and minimizing the spread of infections. This paper explores the application of deep learning techniques in plant disease diagnosis, delving into various models, training methodologies, datasets, real-world applications, and associated challenges. The study highlights the potential of AI-powered disease detection in improving agricultural productivity and sustainability while addressing the technical barriers that need to be overcome for widespread adoption.},
keywords = {Artificial Intelligence, Deep Learning, Plant Disease Diagnosis, Precision Agriculture, Convolutional Neural Networks, Machine Learning, Transfer Learning, Image Processing.},
month = {May},
}