Crop protection and disease detection using artificial intelligence and computer vision: a comprehensive review

被引：1

作者：

Shah K. ^{[1
]}

Sushra R. ^{[2
]}

Shah M. ^{[3
]}

Shah D. ^{[4
,7
]}

Shah H. ^{[4
]}

Raval M. ^{[5
]}

Prajapati M. ^{[6
]}

机构：

[1] Software Engineer, Walmart, Sunnyvale, CA

[2] Atmiya Vidya Mandir, Gujarat, Surat

[3] Department of Chemical Engineering, School of Energy Technology, Pandit Deendayal Energy University, Gujarat, Gandhinagar

[4] Robotics, ECE Concentration, Northeastern University, Boston, MA

[5] Department of Computer Science, Concentration in AI, Hofstra University, Hempstead, NY

[6] Department of Chemical Engineering, S. S Agrawal Institute of Engineering & Technology, Gujarat, Navsari

[7] R &D Engineer, Span Inspection Systems Pvt. Ltd, Gujarat, Gandhinagar

来源：

Multimedia Tools and Applications | 2025年 / 84卷 / 07期

关键词：

Agriculture; Computer vision; Crop; Disease; Machine learning; Protection;

D O I：

10.1007/s11042-024-19205-9

中图分类号：

学科分类号：

摘要：

The technological advancements in the field of agriculture have increased to a great extent in recent years, and many techniques have evolved from other techniques. Some methods are improved or upgraded from the previous versions by implementing a new model or using better hardware devices. This has been helpful for the farmers in increasing crop productivity, and the life expectancy of crops has also increased as the diseases inside or outside the crops can be detected much earlier, and learning at an early stage helps prevent other crops. In this paper, we have presented a study where many varieties of fruits and vegetables have been taken to determine which method was used for a particular crop. By analyzing the various works carried out by the authors, it was inferred that most of the works revolved around image processing and hyperspectral imaging. Due to this, we had also included most of the papers, particularly as the models and hardware components used were much better than other works. Then, a comparative study was done where different fruits and vegetables highlighted the two main areas: the method used and the accuracy obtained. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

引用

页码：3723 / 3743

页数：20

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