review · Frontiers in Plant Science
Plant diseases significantly reduce global food production, and traditional manual detection methods are slow and prone to errors. This review explores recent advancements in using Machine Learning (ML) and Deep Learning (DL) technologies for early and accurate plant disease identification. Focusing on research published between 2015 and 2022, the study highlights how these techniques improve detection accuracy and efficiency. It also addresses common challenges, such as data availability, imaging quality, and distinguishing between healthy and diseased plants. The research offers insights and potential solutions for researchers and industry professionals, providing a comprehensive overview of the field's current state, its benefits, limitations, and implementation hurdles.
Plant diseases threaten global food security by reducing crop yields. This research is important because it reviews how advanced artificial intelligence can help farmers and agricultural experts quickly and accurately identify plant diseases, potentially preventing widespread crop loss and ensuring more reliable food supplies.
This review highlights the potential for developing automated plant disease detection systems using Machine Learning and Deep Learning. Such systems could be used by farmers, agricultural consultants, and large-scale farming operations to monitor crop health efficiently. The research is foundational, identifying both the effectiveness and the current limitations, suggesting it is at an early-to-mid stage of applied research, requiring further development to address data and imaging challenges before widespread commercialisation.
AI-generated from the published abstract. Always read the original work before citing.
Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.
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DOI: 10.3389/fpls.2023.1158933
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