Digital Technologies in Crop Genotype Designing Methods: Scope, Limitations and Future Perspectives
Aaron Chimbelya Siyunda *
Department of Plant Science, School of Agricultural Sciences, University of Zambia (UNZA), P.O. Box 32379, Great East Road Campus, Lusaka, Zambia and Department of Crop Science, Natural Resources Development College (NRDC), P/Bag CH 99, Great East Road, Lusaka, Zambia.
Emmanuel Chikalipa *
Department of Plant Science, School of Agricultural Sciences, University of Zambia (UNZA), P.O. Box 32379, Great East Road Campus, Lusaka, Zambia.
Vinita Ramtekey
ICAR-Indian Institute of Seed Science, Mau, Uttar Pradesh-275103, India.
Ntombokulunga Mbuma
Department of Plant Breeding, Agricultural Research Council, Vegetables, Industrial and Medicinal Plants Institute, Private Bag X293, Pretoria 0001, South Africa and Department of Plant Sciences, Plant Breeding, University of the Free State, P.O. Box 339, Bloemfontein 9300, South Africa.
Mick Mwala
Department of Plant Science, School of Agricultural Sciences, University of Zambia (UNZA), P.O. Box 32379, Great East Road Campus, Lusaka, Zambia.
Natasha Muchemwa Mwila
Department of Plant Science, School of Agricultural Sciences, University of Zambia (UNZA), P.O. Box 32379, Great East Road Campus, Lusaka, Zambia.
Tesfaya Mitika Regassa
Ethiopian Institute of Agricultural Research (EIAR), Holetta, Ethiopia.
Dyness Nshimbi
Department of Crop Science, Natural Resources Development College (NRDC), P/Bag CH 99, Great East Road, Lusaka, Zambia.
*Author to whom correspondence should be addressed.
Abstract
The modern world agricultural sector has come under severe attack from several factors. These factors range from biotic to abiotic factors and they present threats to the environment and the world economies at large. If agricultural production is made more sustainable, it can be able to combat the current food shortages. Looking into the present scenario, there is a great need to improve the traditional breeding designing methods to develop genotypes of different crops that would be able to withstand the current adverse effects brought about by persistent climate change. Central to the basis and key factor of improving the designing methods in crop production are different digital technologies such as Artificial Intelligence (AI), Deep Learning (DL), Machine Learning (ML), Geographical Information System (GIS), Precision Agriculture (PA), and Remote Sensing (RS). The digitalization of traditional breeding strategies has its weaknesses in terms of genetic gains it could offer in improving crop production. However, improving digital technologies would result in improved designing methods of crop production that would consequently result in increasing agricultural production and productivity. Therefore, the current review highlights the gains that have been made especially by AI and ML in designing methods of crop production. In addition, the review also highlights the limitations of these digital tools and their potential in crop designing methods for future crop genetic gains and production as well.
Keywords: Artificial intelligence, crop design methods, digitalization, machine learning, production