Application of Machine Learning: A Recent Advancement in Plant Diseases Detection
Siddhartha Das 1, A,C,E-F
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Department of Plant Pathology, M. S. Swaminathan School of Agriculture, Centurion University of Technology and Management, India
Division of Plant Pathology, Indian Agricultural Research Institute, India
Department of Plant Pathology, College of Agriculture, Odisha University of Agriculture & Technology, 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
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
Submission date: 2022-01-02
Acceptance date: 2022-04-06
Online publication date: 2022-04-25
  • 1. Machine learning is an advance tool kit for plant disease detection.
  • 2. Machine based deep learning has the features of fast, high precision, high training efficiency.
  • 3. Hyperspectral/multispectral imaging technology is robust and effective in the detection of different diseases under smart agricultural technology.
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 meeting 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.
The authors have declared that no conflict of interests exist.