Optimization Accuracy of Crop Specific Model using Machine Learning Technique

Abstract

Agriculture is a critical sector that requires innovative approaches to ensure sustainability and productivity. This study proposes a crop-specific predictive model leveraging the adaptive boosting (AdaBoost) machine learning (ML) technique to address key agricultural challenges such as yield prediction, disease detection, and resource optimization. The model utilizes diverse input features, including climatic variables, soil properties, and crop-specific parameters, to provide accurate and reliable predictions. AdaBoost, known for its robustness and ability to handle non-linear relationships, is employed to analyze complex datasets and derive meaningful insights. Adaptive Boosting, or AdaBoost, is an algorithm aimed at improving the performance of ensembles of weak learners by weighing the data itself as well as the learners. Experimental results demonstrate the model’s effectiveness in enhancing decision-making for farmers and agricultural stakeholders. The findings emphasize the potential of machine learning techniques in modernizing agriculture and contributing to food security.