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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM50363

Harvestify Crop Disease Prediction and Recommendation

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM50363

Volume:

9 |

6 |

Jun 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem50363

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MP

Mrs.M. Parimala

PYR

P. Yogitha Reddy

MT

M. Tejasri

AH

A. Harshitha

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Abstract

As the primary source of income for most Indians, agriculture is a major economic sector in the country. With machine learning (ML) techniques, Harvestify aims to improve farming practices. This research includes the development of a machine learning (ML) system that assists farmers in predicting the optimal harvest times and recommending crops that are suitable for the soil and climate of their area. It also detects and treats plant issues using photo recognition. Harvestify also includes a Soil-depending Profiling System that analyzes data to select crops depending on soil characteristics and rainfall patterns. It suggests using the appropriate fertilizers to build better soil and boost agricultural yields. The system can identify sick leaves and provide treatment recommendations by utilizing machine learning algorithms such as Random Forest and convolutional neural networks. Plant disease detection is an important task. The incorporation of these technologies could lead to improved agricultural resilience, sustainability, and production for millions of Indian farmers.

KEYWORDS

Harvestify Agriculture India Machine Learning Harvesting System Crop Recommendation Soil Based Profile Profiling System Fertilizer Recommendation Disease Detection Image Recognition

Cite This Article

Mrs.M. Parimala, P. Yogitha Reddy, M. Tejasri, A. Harshitha, "Harvestify Crop Disease Prediction and Recommendation," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 6, Jun 2025. https://doi.org/10.55041/ijsrem50363

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Publication Date

6/15/2025

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Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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