Artificial intelligence and deep learning based Technologies for emerging disease recognition and pest Prediction in beans (phaseolus vulgaris l.): A systematic review
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
African Journal of Agricultural Research
Abstract
Artificial Intelligence (AI) and deep learning have the capacity to reduce losses in crop production, such
as low crop yields, food insecurity, and the negative impacts on a country’s economy caused by crop
infections. This study aims to find the knowledge and technological gaps associated with the
application of AI-based technologies for plant disease detection and pest prediction at an early stage
and recommend suitable curative measures. An evidence-based framework known as the Preferred
Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology was used to
conduct systematic reviews of the state-of-the-art of AI and deep learning techniques for crop disease
identification and pest prediction in developing countries. The results demonstrate that conventional
methods for plant disease management face some challenges, such as being costly in terms of labour,
having low detection and prediction accuracy, and some are not environmentally friendly. Also, the
rapid increase in data-intensive and computational-intensive tasks needed for plant disease
classification using traditional machine learning methods poses challenges such as high processing
time and storage capacity. Consequently, this paper recommends a deep learning and AI-based
strategy to enhance the detection, prediction and prevention of crop diseases. These recommendations
will be the starting point for future research.
Description
Research Paper
Keywords
Plant diseases detection, pest prediction, pesticide recommendation, artificial intelligence, machine learning