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

Author:

Michael Pendo John Mahenge,Hussein Mkwazu,Camilius A. Sanga,Richard Raphael Madege,Beatrice Mwaipopo,Caroline Maro

Publisher

Academic Journals

Subject

General Agricultural and Biological Sciences

Reference104 articles.

1. Abawi GS, Ludwig JW, Gugino BK (2006). Bean root rot evaluation protocols currently used in New York. Annual Report- Bean Improvement Cooperative 49:83-84.

2. Abdel-Fattah GM, El-Haddad SA, Hafez EE, Rashad YM (2011). Induction of defense responses in common bean plants by arbuscular mycorrhizal fungi. Microbiological Research 166(4):268-281.

3. Agarwal M, Gupta SK, Biswas KK (2020). Development of Efficient CNN model for Tomato crop disease identification. Sustainable Computing: Informatics and Systems 28:100407.

4. Ahmad I, Hamid M, Yousaf S, Shah ST, Ahmad MO (2020). Optimizing pretrained convolutional neural networks for tomato leaf disease detection. Complexity 6 p.

5. Allen DJ, Ampofo JKO, Wortmann CS (1996). Pests, disease and nutritional disorders of the common bean in Africa: A field guide. Cali, Colombia: International Centre for Tropical Agriculture; Wageningen, The Netherlands: Technical Centre for Agricultural and Rural Co-operation P 37.

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