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<p>Agriculture is one of the sources of income for any country. Sugarcane is a tropical plant under agricultural domain, which grows in the field on an annual basis. Its management from germination to maturity stage is required to be focused while it fights with numerous infectious agents. This study is taken care to focus on the computer-based technologies, their intervention in the growth of the crop, their management, and related to identification of the diseases or stress symptoms available on the leaf’s surface of sugarcane. There are various sugarcane diseases such as red rot, smut, wilt, grassy shoot, ratoon stunting, yellow leaf, and sugarcane rust etc. This review discussed about computer-based technologies like machine learning models (Support Vector Machine), deep learning models (AlexNet, GoogLeNet, InceptionV3 etc), domain specific models (Reverse Transcriptase- Polymerase Chain Reaction (RT-PCR)), and so on. The assertion is made after research, literature analysis, and discovery that every model has distinctive constraints that must be addressed. Each model in their study is showing their best (in terms of accuracy, precision, and recall), but its real time interventions is not up to mark. This paper also highlighted that how abiotic factors is important while in the growth of sugarcane.</p> Graphical abstract <p></p>

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A Survey of Computer Assisted Approaches for Sugarcane Stress Detection and their Management

  • 期刊论文

  • 发表日期：

    2026年5月21日

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  • Manish Sharma,
  • Paramjot Kaur Sarao,
  • Jaspreet Singh

摘要

Agriculture is one of the sources of income for any country. Sugarcane is a tropical plant under agricultural domain, which grows in the field on an annual basis. Its management from germination to maturity stage is required to be focused while it fights with numerous infectious agents. This study is taken care to focus on the computer-based technologies, their intervention in the growth of the crop, their management, and related to identification of the diseases or stress symptoms available on the leaf’s surface of sugarcane. There are various sugarcane diseases such as red rot, smut, wilt, grassy shoot, ratoon stunting, yellow leaf, and sugarcane rust etc. This review discussed about computer-based technologies like machine learning models (Support Vector Machine), deep learning models (AlexNet, GoogLeNet, InceptionV3 etc), domain specific models (Reverse Transcriptase- Polymerase Chain Reaction (RT-PCR)), and so on. The assertion is made after research, literature analysis, and discovery that every model has distinctive constraints that must be addressed. Each model in their study is showing their best (in terms of accuracy, precision, and recall), but its real time interventions is not up to mark. This paper also highlighted that how abiotic factors is important while in the growth of sugarcane.

Graphical abstract