How Machine Learning is Revolutionizing Farming and Boosting Crop Yields

What is it about?

This article provides a systematic literature review (SLR) on the applications of machine learning (ML) in precision agriculture from 2015 to 2023, focusing on how ML techniques such as ensemble learning (EL) and deep learning (DL) are transforming traditional farming practices. It highlights key areas such as disease detection, yield prediction, soil property analysis, and weather forecasting, showcasing ML's potential to enhance agricultural productivity and sustainability. The study examines various ML algorithms like SVM, ELM, and RFs, which are used to predict soil attributes and improve crop management. Despite significant advancements, the article identifies challenges like data availability, model scalability, and the need for regional validation as obstacles to fully realizing the benefits of precision agriculture. The research methodology involved a thorough review of peer-reviewed journal articles published in English over the last decade, focusing on empirical studies that provide original findings and sufficient methodology for analysis. The article underscores the importance of overcoming existing challenges to unlock the full potential of ML in precision agriculture.

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Why is it important?

This review examines the role of machine learning (ML) and related technologies in precision agriculture, highlighting their potential to address critical challenges such as food demand, climate change, and resource limitations. By synthesizing literature from 2015 to 2023, the article emphasizes the transformative impact of ML in enhancing agricultural efficiency and sustainability. The findings underscore the importance of integrating advanced ML techniques with IoT-enabled equipment to revolutionize traditional farming practices, ultimately contributing to global food security and environmental conservation. Key Takeaways: 1. This review article summarizes the application of machine learning in agriculture, focusing on areas like disease detection and yield prediction. It highlights how ML techniques can improve the accuracy and efficiency of identifying crop diseases and predicting crop yields, thereby optimizing farming practices. 2. The review explores the use of ML algorithms for predicting soil properties, such as moisture and organic carbon content, which are vital for effective crop management. It identifies challenges in data quality and variability across regions, emphasizing the need for comprehensive datasets and model validation. 3. This review article compiles recent developments in weather prediction using ML, illustrating its significance in agricultural planning. It stresses the importance of accurate weather forecasting to adapt farming activities to changing climatic conditions, enhancing the resilience and productivity of agricultural systems.