Crop Disease Prediction using Deep Learning Techniques - A Review

International Journal of Computer Science and Engineering

© 2022 by SSRG - IJCSE Journal

Volume 9 Issue 4

Year of Publication : 2022

Authors : Gargi Sharma, Gourav Shrivastava

10.14445/23488387/IJCSE-V9I4P104

How to Cite?

Gargi Sharma, Gourav Shrivastava, "Crop Disease Prediction using Deep Learning Techniques - A Review," SSRG International Journal of Computer Science and Engineering , vol. 9,  no. 4, pp. 23-28, 2022. Crossref, https://doi.org/10.14445/23488387/IJCSE-V9I4P104

Abstract:

In agriculture, AI is bringing about a revolution by replacing traditional methods with more efficient ones and contributing to a better world. Artificial Intelligence and machine learning enable the development and implementation of devices that can identify and control plants, weeds, pests, and diseases through remote sensing. Plant disease lowers the quantity and quality of food, fiber, and biofuel crops, important to the Indian economy. In addition to reducing waste, using Deep learning technologies can increase quality and speed up market access for farmers. Here, we summarize recent crop disease detection research papers. Multiple deep learning algorithms demonstrate the current solutions for different crop disease diagnoses in this research. I hope this report will be useful to other crop disease detection researchers.

Keywords:

Crop Disease, CNN, Deep Learning.

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