REVIEW
Application of machine learning: a recent advancement in plant diseases detection
1
Department of Plant Pathology, M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Paralakhemundi, Odisha, India
2
Division of Plant Pathology, ICAR – Indian Agricultural Research Institute, New Delhi, India
3
Department of Plant Pathology, College of Agriculture, Odisha University of Agriculture and Technology, Bhubaneswar, India
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
Submission date: 2022-01-02
Acceptance date: 2022-04-06
Online publication date: 2022-06-29
Corresponding author
Siddhartha Das
Department of Plant Pathology, M. S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Village Alluri Nagar, R Sitapur, Via- Uppalada, Pa, 761211, Paralakhemundi, India
Department of Plant Pathology, M. S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Village Alluri Nagar, R Sitapur, Via- Uppalada, Pa, 761211, Paralakhemundi, India
Journal of Plant Protection Research 2022;62(2):122–135
HIGHLIGHTS
- Machine learning is an advance tool kit for plant disease detection.
- Machine based deep learning has the features of fast, high precision, high training efficiency.
- Hyperspectral/multispectral imaging technology is robust and effective in the detection of different diseases under smart agricultural technology.
KEYWORDS
TOPICS
ABSTRACT
The world population, and thus the need for food, is increasing every day. This leads to the
ultimate question of how to increase food production with limited time and scarce land.
Another obstacle to meet the food demand includes the stresses a plant goes through. These
may be abiotic or biotic, but the majority are biotic, i.e., plant diseases. The major challenge
is to mitigate plant diseases efficiently, more quickly and with less manpower. Recently,
artificial intelligence has turned to new frontiers in smart agricultural science. One novel
approach in plant science is to detect and diagnose plant disease through deep learning and
hyperspectral imaging. This smart technique is very advantageous for monitoring large
acres of field where the availability of manpower is a major drawback. Early identification
of plant diseases can be achieved through machine learning approaches. Advanced machine
learning not only detects diseases but also helps to discover gene regulatory networks
and select the genomic sequence to develop resistance in crop species and to mark pathogen
effectors. In this review, new advancements in plant science through machine learning
approaches have been discussed.
RESPONSIBLE EDITOR
Przemysław Kardasz
CONFLICT OF INTEREST
The authors have declared that no conflict of interests exist.
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