African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6842

Full Length Research Paper

Artificial intelligence and deep learning based technologies for emerging disease recognition and pest prediction in beans (phaseolus vulgaris l.): A systematic review

Michael Pendo John Mahenge
  • Michael Pendo John Mahenge
  • Department of Informatics and Information Technology, Sokoine University of Agriculture, United Republic of Tanzania.
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Hussein Mkwazu
  • Hussein Mkwazu
  • Department of Informatics and Information Technology, Sokoine University of Agriculture, United Republic of Tanzania.
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Camilius A. Sanga
  • Camilius A. Sanga
  • Department of Informatics and Information Technology, Sokoine University of Agriculture, United Republic of Tanzania.
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Richard Raphael Madege
  • Richard Raphael Madege
  • Department of Crop Science and Horticulture, Sokoine University of Agriculture, United Republic of Tanzania.
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Beatrice Mwaipopo
  • Beatrice Mwaipopo
  • Department of Crop Science and Horticulture, Sokoine University of Agriculture, United Republic of Tanzania.
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Caroline Maro
  • Caroline Maro
  • Department of Crop Science and Horticulture, Sokoine University of Agriculture, United Republic of Tanzania.
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  •  Received: 29 September 2022
  •  Accepted: 15 December 2022
  •  Published: 31 March 2023

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.

Key words: Plant diseases detection, pest prediction, pesticide recommendation, artificial intelligence, machine learning.