International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
www.irjet.net
KRISHIK - CROP RECOMMENDATION, FERTILIZER RECOMMENDATION AND DISEASE DETECTION Jalli Gopi1, Katragadda Trinadh2, Jagarlamudi Aswitha3 , kanneganti Akhil4 , Mrs.Ramya Asa Latha Busi 5 1234Undergraduate students, Department of Computer Science and Technology, Vasireddy Venkatadri Institute of
Technology, Guntur, Andhra Pradesh
5Assistant Professor, Department of Computer Science and Technology, Vasireddy Venkatadri Institute of
Technology, Guntur, Andhra Pradesh --------------------------------------------------------------------------***-----------------------------------------------------------------------
Abstract - The Krishik Project aims to revolutionize
the help of this project, small and big farmers will be benefitted in terms of getting higher production of crop and also good profits with high quality product irrespective of preventing the diseases in the crop from the early days and also by providing the best nutrients and fertilizers in a required Quantity. A big extension to this is that the farmers can interact with this tool inorder to solve their queries based on any part of information that is required for them and interact based on various problems and their queries.
agricultural practices and empower farmers through the implementation of innovative technologies and sustainable farming methods. With the growing challenges faced by farmers in terms of climate change, resource scarcity, and market volatility, the Krishik Project offers a comprehensive solution to improve productivity, profitability, and environmental stewardship. With the help of this project, small and big farmers will be benefitted in terms of getting higher production of crop and also good profits with high quality product irrespective of preventing the diseases in the crop from the early days and also by providing the best nutrients and fertilizers in a required Quantity. A big extension to this is that the farmers can interact with this tool inorder to solve their queries and interact based on various problems.
This project is developed by the algorithms of : 1.Random Forest – Crop Recommendation 2.Decision Tree – Fertilizer Recommendation 3.Convolutional Neural Networks(ResNet Architecture )Disease Detection 4. Search Query – SerpAPI
Key Words: (Farmers friend, Smart Farming, Advanced Techniques of Agriculture)
The advantage of the app is that it can be used in multiple languages. It is made in such a way that user can interact with the app in their native or desired language. The Translator converts the native language to a proper standard language, processes it and the output is also displayed in the same language or the native language.
1. INTRODUCTION India is rich and wealthy country by its majority of the people living on the agriculture sector. This paper presents an integrated approach to revolutionizing agricultural practices through the convergence of crop prediction, fertilizer recommendation, and disease detection technologies. Leveraging machine learning algorithms and data analytics, the proposed system enables farmers to make informed decisions at every stage of the agricultural cycle. By forecasting crop yields based on environmental and historical data, optimizing fertilizer application through personalized recommendations, and detecting crop diseases through image analysis, the system empowers farmers to enhance productivity, maximize resource efficiency, and mitigate risks.
2. LITERATURE SURVEY Tanvi Daware , Pratiksha Ramteke, uzma Shaikh and Smita Bharne in the description “ Crop Guidance and Farmer’s Friend – Smart Farming using Machine Learning"[1] described the machine learning techniques to assist farmers in improving the crop yield ,providing fertilizers properly and also early detection of disease in plants by using the machine learning tools. The paper emphasizes the adoption of technology in agriculture, including the use of sensors, IoT systems, machine learning algorithms, and precision agriculture techniques to improve productivity and efficiency.
By analysing the soil and atmosphere in a given region, the best crop will produce a higher yield and the net yield of the crop can be predicted using Water level, distance depth, and soil ph. Through the seamless integration of these innovative technologies, agricultural stakeholders can cultivate sustainable practices, bolster food security, and thrive in an ever-evolving agricultural landscape. With
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Shima Ramesh and co-authors in “Plant Disease Detection Using Machine Learning"[2] discussed the applications of machine learning techniques, specifically Random Forest, for the detection of diseased and healthy leaves in plants.
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