International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
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Crop Recommendation System Using Machine Learning Shivam Kumar1, Suraj Thakur2, Utsav Shukla3, Vedant Agrawal4, Prof. Rajeev Raghuwanshi5 1Student, CSE-AIML Department, Oriental Institute of Science & Technology, Madhya Pradesh, India 2Student, CSE-AIML Department, Oriental Institute of Science & Technology, Madhya Pradesh, India 3Student, CSE-AIML Department, Oriental Institute of Science & Technology, Madhya Pradesh, India 4Student, CSE-AIML Department, Oriental Institute of Science & Technology, Madhya Pradesh, India 5Professor, CSE-AIML Department, Oriental Institute of Science & Technology, Madhya Pradesh, India
---------------------------------------------------------------------***-------------------------------------------------------------------composition, climatic conditions, and rainfall enables a Abstract - In India, agriculture plays a significant role in
better understanding of crop growth patterns influenced by geographical and environmental factors.
the growth of the nation’s economy and provides employment to a large population. Farmers often face challenges in selecting suitable crops due to limited knowledge of soil nutrients and changing environmental conditions, which adversely affects crop productivity. To address this issue, this paper presents a machine learning– based system that assists farmers in recommending appropriate crops and estimating crop yield based on various agricultural parameters. The proposed system uses features such as soil nutrients, temperature, humidity, rainfall, and soil pH to generate predictions. Supervised machine learning techniques are applied to analyze agricultural data and provide accurate recommendations. The system is implemented as a web-based application using a machine learning backend, enabling farmers to adopt a scientific and data-driven approach to farming. This approach can help improve agricultural productivity and support precision farming practices.
In this work, a machine learning–based predictive system is proposed to assist farmers in selecting appropriate crops and estimating expected yield. The system analyzes parameters such as soil nutrients, temperature, humidity, rainfall, and soil pH to generate recommendations. By identifying nutrient deficiencies and unsuitable crop choices, the proposed system helps in minimizing production inefficiencies. The adoption of such a scientific and data driven approach can significantly enhance farming practices and support sustainable agricultural development.
2. Literary Survey Several research works have been carried out in the domain of crop recommendation and yield prediction using machine learning techniques. Padmakar et al. proposed a crop recommendation system for precision agriculture using soil nutrient data, soil type, and yield information. The system employed ensemble-based machine learning techniques to recommend suitable crops. Algorithms such as Random Tree, CHAID, and Support Vector Machine were used to improve prediction accuracy by combining the strengths of multiple models.
Key Words: Agriculture, Crop Recommendation, Crop Yield Prediction, Machine Learning, Random Forest
1.INTRODUCTION Many developed countries have adopted modern scientific and technological techniques in agriculture to improve productivity and efficiency. These countries extensively use data-driven and automated systems to optimize farming practices. In contrast, agriculture in India still largely depends on traditional methods, despite being one of the major contributors to the nation’s economy. Agriculture plays a significant role in employment generation and contributes substantially to the Gross Domestic Product. With rapid population growth and globalization, the demand for food production has increased considerably.
Solanki et al. presented a crop cultivation prediction system with the primary objective of reducing the risk associated with incorrect crop selection. The study evaluated multiple machine learning algorithms and implemented a k-fold cross validation approach, where the dataset was divided into five subsets to ensure reliable performance evaluation. The authors concluded that Random Forest achieved superior prediction performance compared to other models, followed by Support Vector Regression using the radial basis function kernel.
To meet this growing demand, farmers often rely on excessive use of chemical fertilizers to increase crop yield, which may lead to long-term environmental degradation and soil fertility issues. However, if farmers are provided with accurate information regarding suitable crops based on soil nutrients and environmental conditions, crop losses can be reduced and agricultural productivity can be improved. The availability of data related to soil
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Kumar et al. introduced a supervised machine learning approach for crop yield prediction based on historical agricultural data. Their system analysed previous farming records to estimate future crop yields. The proposed model supported both qualitative and quantitative prediction of
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