article · Discover Agriculture
Agriculture is vital for livelihoods and food security in countries such as Ethiopia, yet meeting rising food demands driven by population growth remains difficult. Emerging digital tools, including machine learning, deep learning, and the Internet of Things, provide pathways to modernise agricultural practices. Implementing these technologies allows farmers to utilise data-driven insights to manage crops, evaluate soil health, and respond to weather conditions. Furthermore, connected devices facilitate the real-time monitoring and control of farming operations, contributing to improved resource allocation, greater productivity, and sustainability. Significant barriers to adoption persist, including poor data quality, limited connectivity, and a requirement for farmer training. Targeted investments and collaborative efforts could address these obstacles, helping agricultural regions enhance food security, reduce poverty, and stimulate broader economic growth through technological integration.
Rising populations place immense pressure on food systems across developing economies. Integrating advanced computing and connected sensors into farming can raise crop yields and protect resources. However, understanding the infrastructure deficits and skills gaps is essential for ensuring that digital interventions successfully translate into improved livelihoods and durable food security for farming communities.
As a review, this work details conceptual applications rather than testing a specific product, placing it at the exploratory stage. Potential applications include data analytics platforms and sensor networks for crop monitoring, soil assessment, and farm automation. The intended end users are agricultural producers, yet commercial viability depends on resolving practical hurdles such as network connectivity, data reliability, and digital literacy among farmers.
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Agriculture holds a crucial position in maintaining livelihoods and securing food sources, particularly in nations such as Ethiopia, where a substantial portion of the population depends on agricultural pursuits. However, meeting the growing demand for food production amidst population growth presents considerable challenges. Recent advancements in technology, particularly in the areas of Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT) offer promising solutions to address these challenges. This paper explores the potential of integrating ML, DL, and IoT technologies in agriculture to revolutionize the sector. By harnessing data-driven insights, farmers can make informed decisions regarding crop management, soil health, and weather patterns, leading to optimized resource allocation and increased productivity. Moreover, IoT devices enable the real-time monitoring and control of agricultural operations, enhancing sustainability and productivity. Despite the opportunities presented by these technologies, there are also challenges to overcome, such as data quality, connectivity issues, and the need for farmer education. However, with concerted efforts and investment, Ethiopia and other agricultural regions can unlock the full potential of ML, DL, and IoT technologies to ensure food security, alleviate poverty, and drive economic development. This review paper offers perspectives on the present status, challenges, and future possibilities regarding the integration of ML, DL, and IoT in agriculture. It underscores the transformative potential of these technologies within the sector.
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DOI: 10.1007/s44279-024-00066-7
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