review · Algorithms
Cereal crops make up over two-thirds of the human diet and occupy more than half of the world's cultivatable land, but damaging diseases cause substantial losses in annual yield. Timely identification and quantification of disease severity are critical priorities for safeguarding production. A synthesis of forty-five studies published over five years examines machine learning applications across six major cereal crops. The assessment highlights deep convolutional neural networks applied to hyperspectral imaging data as the most effective method for detecting crop diseases at an early stage. Additionally, transfer learning stands out as the most widely adopted and highest-performing training technique across the reviewed studies. A major obstacle constraining research and deployment in this domain remains the scarcity of comprehensive publicly available datasets for cereal crops.
Cereals provide the foundation of global food security, accounting for over two-thirds of human nutrition. Plant diseases threaten this supply, leading to extensive harvest losses. Identifying which automated machine learning techniques and imaging data provide the most accurate early detection helps direct research towards reliable agricultural monitoring systems that can protect staple food production.
These insights can inform software developers building precision agriculture tools and automated diagnostic systems for agronomists or farmers. The optimal combination of transfer learning, deep convolutional neural networks, and hyperspectral imagery outlines a viable technical architecture for commercial diagnostic tools. However, the work sits at an early review stage, and the scarcity of public datasets represents an immediate hurdle for training commercial-grade models.
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Cereals are an important and major source of the human diet. They constitute more than two-thirds of the world’s food source and cover more than 56% of the world’s cultivatable land. These important sources of food are affected by a variety of damaging diseases, causing significant loss in annual production. In this regard, detection of diseases at an early stage and quantification of the severity has acquired the urgent attention of researchers worldwide. One emerging and popular approach for this task is the utilization of machine learning techniques. In this work, we have identified the most common and damaging diseases affecting cereal crop production, and we also reviewed 45 works performed on the detection and classification of various diseases that occur on six cereal crops within the past five years. In addition, we identified and summarised numerous publicly available datasets for each cereal crop, which the lack thereof we identified as the main challenges faced for researching the application of machine learning in cereal crop detection. In this survey, we identified deep convolutional neural networks trained on hyperspectral data as the most effective approach for early detection of diseases and transfer learning as the most commonly used and yielding the best result training method.
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DOI: 10.3390/a15030075
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