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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 10 Issue: 04 | Apr 2023

p-ISSN: 2395-0072

www.irjet.net

SOIL FERTILITY AND PLANT DISEASE ANALYZER Shivamkumar Prasad1, Sandip Yadav2, Nilesh Gahlot3, Aadarsh Mishra4, Sonali Padalkar5 1Student VIII SEM, B.E., IT Engineering, SLRTCE, Mumbai, India 2Student VIII SEM, B.E., IT Engineering, SLRTCE, Mumbai, India 3Student VIII SEM, B.E., IT Engineering, SLRTCE, Mumbai, India 4Student VIII SEM, B.E., IT Engineering, SLRTCE, Mumbai, India 5Assistant Professor, Department of Information Technology, SLRTCE, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - Agriculture forms an integral part of our lives

Overall, the proposed system has the potential to revolutionize the way farmers make decisions about which crop to grow and help them overcome the challenges associated with plant diseases and soil fertility issues with the right support and investment this system could be a game-changer for the Indian agricultural sector and contribute to the economic growth and development of the country

and is a major source of employment in India, with more than half of the population relying on it. It serves as the backbone of our economy, but the yield of crops depends on several factors, with soil quality and plant diseases being the most significant. Early detection of diseases is critical for achieving an efficient crop yield, as bacterial spots, late blight, Septoria leaf spots, and yellow-curved leaf diseases can all hurt crop quality. For better crop growth, it is imperative to have efficient soil fertility prediction and early plant disease analyzer systems in place. Additionally, automatic methods for classifying plant diseases help take prompt action upon detecting symptoms of leaf diseases. Improving crop yield prediction techniques can aid farmers and other stakeholders in making better decisions regarding agronomy and crop selection, taking into account factors such as temperature, humidity, pH, rainfall, and crop name from previous historical data. This system can provide an accurate status of plant diseases and recommend bettersuited crops for the soil.

2. LITERATURE REVIEW Paper1: Kiran Moraye, Aruna Pavate, Suyog Nikam and Smit Thakkar [7] The research paper has utilized a 10-fold cross-validation technique to develop a model that can accurately predict the correlation between climate and crop yield. The accuracy of the model was found to be 87%, which is a promising result. However, the model only considered climate factors and did not account for other essential factors like soil quality, pests, and chemicals used, which significantly impact crop yield. Therefore, it is crucial to incorporate these factors into the model to develop a comprehensive decision-making tool that farmers can use to select the appropriate crop for their fields. The web application that the researchers propose will be an excellent tool for farmers and users to make better decisions based on the climate of a particular season. By providing recommendations for the most suitable crops based on the prevailing weather conditions, farmers can optimize their yield and reduce the risk of crop failure.

Key Words: Agriculture, for farmers, Machine Learning, Crop recommendation, Real-time detection.

1. INTRODUCTION The proposed system would leverage advanced technologies such as machine learning artificial intelligence and data analytics to analyze large volumes of data and provide accurate recommendations to farmers the system would utilize historical data and real-time data from sensors installed in the fields to provide timely and accurate recommendations to farmers the system would be user-friendly and accessible through mobile and web applications making it easy for farmers to access the recommendations from anywhere at any time. By providing accurate recommendations to farmers the proposed system would not only help farmers make informed decisions about which crop to grow but also improve their yields and profitability the system would also contribute to the overall food security of the country by ensuring that farmers grow the most suitable crops and reducing the risk of crop failure due to plant diseases or soil fertility issues.

Impact Factor value: 8.226

Furthermore, the application will also be useful for policy planners in areas like import-export, pricing, and marketing. By providing early predictions of the yield for different crops, the application can help policymakers make informed decisions even before the crop is harvested. In conclusion, while the research paper has provided a promising model for predicting the correlation between climate and crop yield, it is essential to incorporate other essential factors like soil quality and pests to develop a comprehensive decision-making tool. The proposed web application has the potential to be an invaluable tool for farmers and policymakers alike, providing early insights into the crop yield for different crops based on prevailing weather conditions.

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