International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
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CROP RECOMMENDATION SYSTEM USING ML ALGORITHMS Dr John Jaidhan B1, Edara Bindu Madhavi2, Nandini Sarkar3, Maddila Chandra Shekhar4 and Chaitanya Adduri5 1Professor, Dept. of CSE, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India. 2345Student, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India.
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Abstract - Agriculture is the backbone of India's
In today's modern era, farmers are leveraging advanced technologies to enhance crop yields. While extensive research has been conducted to comprehend the intricate factors influencing agriculture, the collection of comprehensive data remains a hurdle, often resulting in inconsistent and incomplete information. There is a pressing need to refine methodologies for data availability and accessibility to overcome these obstacles. Enter crop recommendation systems, offering a promising solution to these challenges. These systems bring a myriad of benefits to the agricultural sector. Driven by data-driven algorithms and the principles of precision agriculture, crop recommendation systems empower farmers to make well-informed decisions regarding crop selection, thereby maximizing their yield potential. Embracing sustainable farming practices takes precedence, aiming to minimize environmental impact and promote soil health. These systems are easily accessible through user-friendly platforms, enabling farmers to leverage the advantages of technology and data. Ultimately, this contributes to heightened agricultural productivity, financial profitability, and enhanced global food security. In essence, crop recommendation systems play a pivotal role in the modernization of agriculture, fostering sustainability, and improving the livelihoods of farmers worldwide. They serve as a beacon of hope in an industry crucial to our well-being and the future of our planet
economy, crucial for the well-being of its people. Ensuring the production of high-quality crops is essential for maintaining a healthy lifestyle. Analyzing environmental and soil conditions, including factors such as moisture and pH levels, temperature, and chemical composition, is vital for cultivating superior crops. Predicting crop yields has become increasingly challenging due to unpredictable weather patterns caused by global warming, resulting in crop destruction, food scarcity, and tragic consequences such as farmer suicides. This study aims to develop a website utilizing machine learning models for crop recommendations, taking into account inputs such as pH values, temperature, and soil parameters. Various machine learning algorithms, including SVM, logistic regression, naive bayes, and Random Forest, are utilized, with Random Forest demonstrating superior prediction capabilities. These systems carefully analyse diverse factors, including soil quality, climate data, and past crop performance, to suggest optimal crops tailored to specific locations. Accessible through user-friendly platforms, crop recommendation systems empower farmers to harness the benefits of technology and data, thereby enhancing agricultural productivity, financial gains, and global food security. Ultimately, these systems serve as a crucial tool in advancing modern agriculture, leveraging technology and data analysis to assist farmers in making decisions that contribute to food security and agricultural sustainability.
2.LITERATURE SURVEY Today's technological advancements enable the meticulous analysis of agricultural data [1], granting farmers greater control over their crops and empowering them to make informed decisions. Studies in artificial intelligence and machine learning have revolutionized the automation and enhancement of crop quality [1]. These technologies offer insights into crucial aspects such as irrigation management, climate adaptation, and soil nutrition, which significantly impact crop production [2]. Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), in combination with other techniques, are instrumental in classifying and predicting crop yields In-depth research has led to the recognition of CNN as a prominent deep learning algorithm in crop yield prediction, alongside LSTM (Long Short Term Memory) and DNN (Deep Neural Networks) [3]. Machine learning assumes a pivotal role in Crop Yield Prediction (CYP),
Key Words: Random Forest, machine learning model, moisture and pH level, temperature, and chemical composition, recommendation system, factors.
1.INTRODUCTION Agriculture serves as the backbone of India's economy, supporting the livelihoods of a significant portion of the population. However, it remains susceptible to the unpredictable nature of weather patterns, climate variations, and ecological factors. These uncertainties pose substantial challenges to cultivating robust crops. With the population steadily increasing and poverty levels on the rise, it becomes imperative to equip ourselves with effective tools to address these critical issues and offer recommendations to mitigate the adverse effects of poverty, climate change, and erratic harvests.
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