Digitalizing Agriculture for Sustainable Crop production

Siyunda, Aaron Chimbelya; Chikalipa, Emmanuel; Mfune, Tibonge; Habvumba, Rodrick

doi:10.5281/zenodo.6462310

Cited by 1 publication

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“…A re-look at how productivity and production in agriculture, are addressing the issue of food shortage currently and in the future is something that cannot be argued anymore. Especially that, the world's population is expected to rise by approximately 9.6 billion by 2050 [1]. Any efforts designed to overcome the current and future challenges that are affecting or have the potential to affect agricultural production and productivity negatively should be encouraged [2].…”

Section: Introductionmentioning

confidence: 99%

Digital Technologies in Crop Genotype Designing Methods: Scope, Limitations and Future Perspectives

Siyunda,

Chikalipa,

Ramtekey

et al. 2023

AJRCS

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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.

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Section: Introductionmentioning

confidence: 99%