Advancements in Precision Agriculture: A Literature Review of Machine Learning Applications for Crop Monitoring and Yield Prediction

One of the main economic sectors in the world is agriculture. The need to increase productivity, ensure food security and reduce waste has led to the application of modern technologies in agriculture. Increasing productivity, lessening the impact on the environment, and improving livelihoods are the objectives of revolutionizing agriculture through the use of innovative technologies, environmentally friendly methods, and efficient systems. Machine learning (ML) and Deep learning (DL) have demonstrated significant promise in recent years for precision agriculture. This paper discusses Advancements in Precision Agriculture: A Literature Review of Machine Learning Applications for Crop Monitoring and Yield Prediction. In precision agriculture system, to predict crop health monitoring and yield prediction includes soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection, species identification, and nitrogen status estimation are reviewed. Furthermore, the review explores the integration of ML/DL models with IoT-enabled farm machinery to enhance livestock production by predicting fertility patterns, diagnosing eating disorders, and monitoring cattle behavior using collar sensors. Additionally, the review discusses the implementation of intelligent irrigation and harvesting techniques, which reduce human labor while optimizing resource utilization. By analyzing the literature, this review demonstrates the potential of knowledge-based agriculture to improve sustainable productivity and product quality. Moreover, it identifies challenges and opportunities for future research, highlighting the need for interdisciplinary collaborations and ethical considerations in deploying ML- based solutions in agricultural settings. Overall, this review contributes to the understanding of how ML and DL technologies are revolutionizing precision agriculture, paving the way for informed decision-making, resource optimization, and enhanced sustainability in food production systems. This paper provides a comprehensive survey of existing papers and applications of ML in agriculture. In order to demonstrate the numerous uses of ML, the author reviewed 100 publications that were published between 2020 and 2024, among them 53 ML/DL-based algorithms were used in revolutionizing precision agriculture, paving the way for informed decision-making, resource optimization, and enhanced sustainability in food production systems.