This research work present a website to employ Machine Learning [ML] algorithms combined with historical weather information to determine the most profitable crop under the current weather …

Machine learning crop prediction is an application of artificial intelligence that enables farmers to make more informed crop management decisions. It involves using historical data on weather …

Apr 14, 2023 · In this work, various machine learning (ML) algorithms are applied to predict the yield of 'rice and sorghum (jowar)' and a novel weighted feature approach with a combination …

This paper proposes an IoT-based system to predict crop yield based on climatic, soil, and biological factors using parallelized granular support vector machines.

Four methods of machine learning are used to predict crop yield: (i) Ridge Regression,(ii)K-nearest Neighbors Regression, (iii) Support Vector Machines Regression and (iv) Gradient …

Feb 13, 2022 · In this paper, we made an effort to show the crop production analysis processed by implementing both the algorithms—extreme gradient boosting algorithm (XGBoost) and …

Apr 11, 2024 · Crop yield prediction (CYP) is a problematic issue in the agricultural field. The proposed work constructs a hybridization of the XGBoost-SVM-C4.5 framework to forecast …

Accurate crop yield prediction is essential for enhancing food security and optimizing agricultural management. This study explores the effectiveness of various machine learning (ML) …

Prediction using granular SVM can be parallelized using appropriate techniques like MapReduce/GPGPU. IoT-based agriculture increases crop yield by accurate prediction, …

Accurate crop prediction is essential to maximize yield and optimize resource usage, ensuring food security and economic stability. This paper presents an advanced model that integrates …