Harvestify Crop Disease Prediction and Recommendation
1Mrs.M. Parimala
Associate Professor, Dept Computer Science and Engineering Vignan’s Institute of Management and Technology for Women, Hyd Email: pari.parillu@gmail.com
3P. Yogitha Reddy
UG Student, Dept Computer Science and Engineering Vignan’s Institute of Management and Technology for Women, Hyd
Email: p.yogitha2003@gmail.com
2M. Tejasri
UG Student, Dept Computer Science and Engineering Vignan’s Institute of Management and Technology for Women, Hyd
Email: tejasrimadas@gmail.com
4A. Harshitha
UG Student, Dept Computer Science and Engineering Vignan’s Institute of Management and Technology for Women, Hyd
Email: anuguharsitha03reddy@gmail.com
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.
#62/1, New No 7, 1st Cross, 2nd Main,