International Journal of Research Publication and Reviews, Vol 6, Issue 7, pp 171-181 July 2025 International Journal of Research Publication and Reviews Journal homepage: www.ijrpr.com ISSN 2582-7421 Artificial Intelligence in Sustainable Crop Production: A Review of Digital Transformation in Agriculture Babarinde Taofeek Olajide 1 , Toluwanimi Williams Olatokun 2 , Okafor Jervis Tochukwu 3 , Peter Paul Issah 4 , Somtochukwu Ekechi 5 , Chijioke Cyriacus Ekechi 6 1 Department of Agricultural Economics, Ladoke Akintola University of Technology, Ogbomoso, Nigeria. ID: 0009-0003-1899-770X , babarindeolajide88@gmail.com, 2 Mechatronics Engineering, Abiola Ajimobi University, Ibadan. olatokuntoluwanimi@gmail.com 3 Department of Agriculture and Bioresource Engineering, Nnamdi Azikiwe Federal University, Awka. https://orcid.org/0009-0007-0734-2555, okaforjervis@gmail.com 4 Department of Computer Science, Kwame Nkrumah University of Science and Technology. 0009-0008-6774-1141, issahpeterpaul@gmail.com, 5 Department of Information Technology, Fanshawe College of Applied Arts and Technology. https://orcid.org/0009-0003-2083-4838, ekechisomto@gmail.com 6 Department of Electrical and Computer Engineering, Tennessee Technological University. 0009-0006-8920-6719, chijiokekechi@gmail.com ABSTRACT Global agricultural systems face significant pressure to improve productivity and ensure sustainability in light of climate variability, degradation of soil, and the anticipated rise in world population to approximately ten billion in the next 25 years. This analysis looks at how artificial intelligence (AI) might improve sustainable grain production, with a focus on poor nations, especially those in Africa. AI-driven advancements in crop breeding, irrigation control, and pest management are included in the scope, along with how they complement the objectives of Climate Smart Agriculture (CSA). The review, which focuses on earlier research, finds that AI technologies are essential for enhancing resource allocation, decision making, and overall farm productivity. It also highlights certain challenges affecting the way of general acceptance. Among these difficulties include issues with infrastructure, small levels of digital literacy, inadequate data, alongside inadequate policy backing. The results highlight the significance of creating inclusive, localized AI models that are adapted to socioeconomic and agro-ecological circumstances. According to this analysis, AI has a lot of potential to improve sustainable food production, but its application requires an integrated policy framework and capacity building. To guarantee fair and long-lasting AI integration throughout agricultural systems, future research should concentrate on scaling sensitive solutions, improving data ecosystems, and promoting multi-stakeholder collaboration. Keywords: Artificial Intelligence, Sustainable Agriculture, Crop Production, Digital Transformation, Precision Agriculture 1.0 Introduction The total population of the world is expected to reach over ten billion in the next 25 years (2050), increasing farming production in a state of moderate financial development by roughly 50% from 2013 (FAO, 2017). At the moment, about 40% of the land is used for crop cultivation. Farming contributes significantly to the national GDP and job creation. It is not only actively participates in the economies of emerging countries but also contributes substantially to the thriving of established nations. Agricultural augmentation has led to a significant increase in the per capita income of the rural region, so it will be counterproductive and logical to give the agricultural industry more attention. In countries like India, the agricultural industry accounts for 50% of the total workforce and 18% of GDP. Development related to agriculture will promote communal development, which will consequently lead to transition in rural communities and, consequently, structural shifts (Mogili and Deepak, 2018; Shah et al., 2019). Since the advent of technology, many industries have undergone significant change (Kakkad et al., 2019). Agricultural industry, although the least digitalized, has surprisingly seen an improvement in the evolution and adoption of smart farming. Artificial intelligence has started to play an important part in our everyday activities, with the potential to change our surroundings and expand our perceptions (Kundalia et al., 2020; Gandhi et al., 2020; Ahir et al., 2020). Plessen (2019) introduced an agricultural organizing technique that combines truck route with crop assignment. The workforce, previously limited to a minor industrial sector, is now chipping in to several industries due to these emerging innovations. Agricultural operations comprise a variety of jobs, including choosing fertilizer, controlling irrigation, evaluating soil health, and making decisions related to crops. Two machine learning methods that are very good at handling large volumes of multidimensional data are neural networks and random forests (Niazian and Niedbała, 2020). These methods enhance genotype classification, yield prediction, and in vitro breeding optimization. Combining machine learning with images enables precision phenotyping, which