Smart farming and artificial intelligence in East Africa: Addressing indigeneity, plants, and gender

Foster, Laura A.; Szilagyi, Katie; Wairegi, Angeline; Oguamanam, Chidi; Beer, Jeremy de

doi:10.1016/j.atech.2022.100132

Cited by 17 publications

(6 citation statements)

References 27 publications

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“…From pest and disease detection to crop monitoring in remote areas and data-driven decision-making tools for smallholder farmers, AI is proving to be a transformative force in enhancing productivity and sustainability. As initiatives continue to evolve, there is potential for further collaboration, knowledge-sharing, and the creation of tailored solutions that empower African farmers, strengthen food security, and contribute to the overall advancement of agriculture across the continent (Foster, et al, 2023;Vincent et al, 2021).…”

Section: Developments In Ai In Agriculture: Africamentioning

confidence: 99%

AI in agriculture: A comparative review of developments in the USA and Africa

Olabimpe Banke Akintuyi

2024

Open Access Res. J. Sci. Technol.

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This comparative review explores the advancements and applications of Artificial Intelligence (AI) in agriculture, focusing on the developments in the United States (USA) and Africa. The integration of AI technologies in agriculture has witnessed significant progress globally, addressing challenges and transforming traditional farming practices. In the USA, precision agriculture and smart farming techniques driven by AI have become integral components of modern agricultural systems. These innovations include autonomous machinery, drone technology for crop monitoring, and predictive analytics for yield optimization. In contrast, the application of AI in African agriculture presents a distinct set of challenges and opportunities. The review delves into initiatives aimed at leveraging AI to enhance agricultural productivity, improve resource management, and address food security concerns in various African nations. These efforts include the deployment of AI for pest and disease detection, crop monitoring in remote areas, and the implementation of data-driven decision-making tools to support smallholder farmers. The comparative analysis sheds light on the disparities in AI adoption between the USA and Africa, emphasizing factors such as infrastructure, technological accessibility, and resource availability. Additionally, it explores collaborative efforts and partnerships that bridge the gap and contribute to the sustainable development of AI in African agriculture. As both regions navigate the complexities of implementing AI in agriculture, this review underscores the potential for technology to play a pivotal role in addressing global food challenges. The findings highlight the need for tailored approaches, policy frameworks, and international collaborations to ensure inclusive and equitable access to AI-driven innovations in agriculture, fostering a shared commitment to sustainable and technologically empowered farming practices.

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Section: Developments In Ai In Agriculture: Africamentioning

confidence: 99%

AI in agriculture: A comparative review of developments in the USA and Africa

Olabimpe Banke Akintuyi

2024

Open Access Res. J. Sci. Technol.

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“…However, different technologies may not be compatible with each other or with existing agricultural machinery and equipment, which could limit the adoption of advanced technologies in precision agriculture [2,8,84]. Furthermore, the presence of bias and discrimination intertwined with information technology, education, risk-taking attitudes, and western power structures constitute formidable obstacles, hindering the equitable dissemination and advancement of smart-farming technologies, particularly within developing nations [80,85]. This highlights the need for policies on data sharing that cater to both the public and farming industries and are sufficient to ensure data security [20].…”

Section: Barriers To Adapting New Technologies In Precision Agriculturementioning

confidence: 99%

The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture

Karunathilake,

Le,

Heo

et al. 2023

Agriculture

178

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Precision agriculture employs cutting-edge technologies to increase agricultural productivity while reducing adverse impacts on the environment. Precision agriculture is a farming approach that uses advanced technology and data analysis to maximize crop yields, cut waste, and increase productivity. It is a potential strategy for tackling some of the major issues confronting contemporary agriculture, such as feeding a growing world population while reducing environmental effects. This review article examines some of the latest recent advances in precision agriculture, including the Internet of Things (IoT) and how to make use of big data. This review article aims to provide an overview of the recent innovations, challenges, and future prospects of precision agriculture and smart farming. It presents an analysis of the current state of precision agriculture, including the most recent innovations in technology, such as drones, sensors, and machine learning. The article also discusses some of the main challenges faced by precision agriculture, including data management, technology adoption, and cost-effectiveness.

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“…Cloud computing offers services such as cloud storage and access to data. To tackle problems related to centralized mode of cloud computing, edge computing is emerging as efficient data processing and it works in combination with IoT and cloud services [2,11]. Recently, cloud and edge computing techniques are widely being applied in smart agriculture e.g.…”

Section: Major Technologies For Smart Agriculture Systemsmentioning

confidence: 99%

“…Digital technologies in smart farming, based on application of big data analysis, IoT, AI, remote sensing, robotic and smart sensors can help to use real time information accurately and improve yield as well as productivity [11,12]. In this context, data driven approaches using large quantities of sensor-based generated data (big data) can be used to develop early warning, farm management and decision-making systems for improved soil characteristics, water use efficiency, yields, and health assessments of crop, animal and horticultural products.…”

Section: Introductionmentioning

confidence: 99%