International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 10 | Oct 2024
p-ISSN: 2395-0072
www.irjet.net
Crop Recommendation System Based On High Yield Using Machine Learning Techniques Maria Sobana S1, Shyla Shree M M2, Susmitha M3, Swetha M4 1 Assistant Professor and Faculty Mentor, Dept of Computer Science and Engineering, K L N College of Engineering,
Pottapalayam, Sivagangai, Tamil Nadu, India
2,3,4Student, Dept of Computer Science and Engineering, K L N College of Engineering, Pottapalayam, Sivagangai,
Tamil Nadu, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Agriculture is the backhand of the Indian
composition of soils, which can more directly lead to a decrease in losses due to nutrient deficiencies. We use models like Random Forest, XGBoost, and SVM to analyze environmental traits and improve crop selection through yield forecasting. Such algorithms provide suitable insights for higher yields and productivity. They detect nutrient deficiencies and recommend soil amendments.
economy, providing employment to an excellent extent and generating an outstanding amount in the country's income. It plays a vital role in providing food and it supports rural livelihoods. Using machine learning for improving agricultural productivity through the Crop Recommendation System based on High Yield crop offers exact advice to farmers. It analyzes crucial factors like soil type, climate, location, and real-time weather data temperature, rainfall, humidity, and altitude to determine the best crops that can be planted for specific regions and seasons. Machine learning algorithms give it an ability to recognize some specific patterns within the data to predict which crops will most likely bring better results in a given condition. It ranks the top five crops according to predicted yields so that farmers can quickly identify the best crop for their farm. Such a system is scalable and adaptable, allowing it to be applied across an vast spectrum of agricultural practices and regional diversities. Such a system, aligning crop recommendations with local environmental factors, thus promotes sustainable farming, boosts food production, and optimizes resource use, thereby aiding the farmers to decide whether to use whatever means to improve both crop selection and the general farm productivity.
Crop recommendation and environmental analysis is meant to link historical systems with modern scientific techniques in Indian agriculture by equipping the farmers with the ability to decide for higher output and sustainability.
2.LITERATURE SURVEY [1]Training sample selection for robust multi-year withinseason crop classification using machine learning - Zitian Gao*, Danlu Guo*, Dongryeol Ryu*, Andrew W. Western. The system decides on optimizing training sample selection by adjusting parameters like size and class balance. The use of Random Forest and SVM algorithms makes accurate predictions. The model addresses the problem of using past season data that can decrease accuracy with unseen years due to weather variations. These techniques enhance crop recommendations that are climate and environment responsive while performing well in various seasons.
Key Words: Crop recommendation, Crop Yield Prediction, Machine Learning, Random Forest, Support Vector Machine(SVM), XGBoost.
[2]Crop recommendation and forecasting system for Maharashtra using machine learning with LSTM: a novel expectation-maximization technique - Yashashree Mahale1, Nida Khan1, Kunal Kulkarni, Shivali Amit Wagle, Preksha Pareek, Ketan Kotecha, Tanupriya Choudhury, Ashutosh Sharma. This system is set based on the optimized dataset through the EM technique. The accuracy level is better. The Random Forest algorithm with a 92% accuracy level states that this one is well effective for prediction regarding suitable crops. The focus is also limited to Maharashtra. A highly region-specific model will be arrived at, but the applicability of the results will be limited to other regions due to varied climatic conditions and agricultural practices.
1.INTRODUCTION Many countries have become highly advanced in agricultural practices: they have used scientific discoveries to improve their efficiency and yield in order to benefit the agribusiness in their country. India, on the other hand, has remained the same in farming techniques used and remains the biggest sector contributing to its GDP in agriculture. Obsolete methods are not helping in increasing productivity in farming, but there's a deep need for a statistic-based technique that could guide a farmer into appropriate crops selection for his farmland. It generates estimates on crop choice based on environmental indicators such as rainfall, weather, and
© 2024, IRJET
|
Impact Factor value: 8.315
|
ISO 9001:2008 Certified Journal
|
Page 687