Advances in agricultural bioinformatics: an outlook of multi “omics” approaches

Singh, Nisha; Ujinwal, Megha; Singh, Anuradha

doi:10.1016/b978-0-323-89778-5.00001-5

Cited by 2 publications

(2 citation statements)

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“…One of the most promising and efficient ways to increase agricultural production among the smallholder farmers in Zimbabwe is through digitalisation of agriculture. Agri-technology and precision farming, which can be referred to as digital agriculture, is a new scientific field that uses data intensive approaches to drive agricultural productivity (Singh, Ujinwal, & Singh, 2022). In digital agriculture, data is generated using current agricultural processes and obtained from various sensors that allow a better appreciation of the operation environment (an interaction of dynamic crop, soil, and weather conditions) Figure 1 and the procedure itself (machinery data), leading to more precise and faster decision making (Benos et al, 2021).…”

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“…In digital agriculture, data is generated using current agricultural processes and obtained from various sensors that allow a better appreciation of the operation environment (an interaction of dynamic crop, soil, and weather conditions) Figure 1 and the procedure itself (machinery data), leading to more precise and faster decision making (Benos et al, 2021). However, literature has showed a wide gap on the level of digitalisation and complexity of agriculture between the developing and developed countries (Gayatri et al, 2016;Singh et al, 2022). The digital agriculture in the developed countries is more complex and advanced compared to that in the developing countries.…”

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Digitalisation of agriculture in Zimbabwe: Challenges and opportunities

Parwada

Marufu²

2023

ijsar

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To provide insights on the potential of digitalised agriculture in improving output exploring its challenges among the smallholder farmers in Zimbabwe. Literature on the application of digital agriculture was gathered. The research from countries with same or similar Zimbabwean agricultural conditions were then selected in this review. Notably, there are disparities in complexity and level of digitalisation between the developing and developing countries. Unlike in the developing countries, digitalised agriculture is more advanced and highly applied in developed countries. In Zimbabwe specifically, application of digitalized agriculture is skewed towards the commercial farmers than the smallholder communal farmers. The application of digital agriculture e-agriculture) has gained momentum world over in recent years but still low in Zimbabwe where it is more common to the highly literate and resource endowed farming communities than poorly resourced farmers. The digital agriculture is a useful modern technology applied in agricultural production systems in enhancing precision application of resources e.g water, fertilizers, pesticides etc increasing the technical efficiency that translates into high farm outputs (both quantity and quality). Machine Learning (ML) which is a subset of Al, developed to handle various challenges faced during the formation of knowledge-based farming systems. Therefore, digitalisation of agriculture ranges from the use of simple offline programmed production systems installed into information and communications technology (ICTs) gadgets to complex algorithms run by computers. In advanced digitalisation, algorithms are applied in different agronomic practices of crops as well as in animal husbandry.

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confidence: 99%