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This comprehensive survey study meticulously reviews the intersection of artificial intelligence (AI) and agriculture. Focusing on four key domains—soil testing, crop recommendation using machine learning, fertilizer recommendation through machine learning, and crop disease detection via computer vision and deep learning—this study examines a range of innovative methodologies and models. These encompass decision trees, support vector machines, neural networks, regression analysis, clustering methods, ensemble techniques, and convolutional neural networks. By delving into these studies, this chapter offers a holistic understanding of the current landscape of AI applications in agriculture, providing insights into the methodologies, datasets, and outcomes of each domain. It also underscores the transformative potential of AI in reshaping agricultural practices for enhanced sustainability, productivity, and resource management.

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An Extensive Review of Soil Testing, Crop Suggestion, Fertilizer Advice, and Disease Prediction Methods Using Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Rambabu Pemula,
  • A. Obulesh,
  • K. Chithra,
  • G. Sravan Reddy,
  • K. Pranay,
  • B. Anjali

摘要

This comprehensive survey study meticulously reviews the intersection of artificial intelligence (AI) and agriculture. Focusing on four key domains—soil testing, crop recommendation using machine learning, fertilizer recommendation through machine learning, and crop disease detection via computer vision and deep learning—this study examines a range of innovative methodologies and models. These encompass decision trees, support vector machines, neural networks, regression analysis, clustering methods, ensemble techniques, and convolutional neural networks. By delving into these studies, this chapter offers a holistic understanding of the current landscape of AI applications in agriculture, providing insights into the methodologies, datasets, and outcomes of each domain. It also underscores the transformative potential of AI in reshaping agricultural practices for enhanced sustainability, productivity, and resource management.