Journal Article10.12928/telkomnika.v23i2.26621
Advanced crop yield prediction using machine learning and deep learning: a comprehensive review
Ayush Anand,Kavita Jhajharia +1 more
2
About: This article is published in TELKOMNIKA Telecommunication Computing Electronics and Control. The article was published on 01 Apr 2025.
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Citations
Weather-driven groundnut price forecasting and profitability assessment of cropping patterns in Tamil Nadu using boosting algorithms
Kalpana Muthuswamy,Shrishail Dolli,Kedar Khandeparkar,Chandre Gowda,Venkatesa Palanichamy Narasimma Bharathi,K M Shivakumar,C. S. Sumathi,Suresh Appavu,Balakrishnan Natarajan,Kalpana Muthuswamy,Shrishail Dolli,Kedar Khandeparkar,Chandre Gowda,Venkatesa Palanichamy Narasimma Bharathi,K M Shivakumar,C. S. Sumathi,Suresh Appavu,Balakrishnan Natarajan +17 more
Machine learning in agricultural technology: optimizing crop yield prediction
Navin Prakash
Abstract: Agriculture is the main source of income for Indian farmers in the developing world. The current agricultural system relies heavily on technology to produce more and earn profits, but agriculture still depends on seasonal aspects. It is very difficult to identify the type and amount of pesticide needed for plant growth. Farmers face several serious challenges, including: (i) adapting to climatic conditions due to soil erosion and pollution; (ii) soil deficiencies, such as lack of potassium, phosphorus, nitrogen and other minerals, can have a negative impact on crop yields; and (iii) given the diversity of soils in the world, farmers make serious mistakes about which crops to grow in which type of soil, which is a very important factor to understand.