Rapid growth of population, diminishing natural resources, climate change, shrinking agricultural lands and unstable markets are making the global food systems rather insecure. Therefore, modern agriculture and food systems should be more productive in terms of output, efficient in operation, resilient to climate change and sustainable for the future generations. As a result, the need of a technological transformation is greater than ever before. Being a recent advancement in computer sciences, Artificial Intelligence (AI) has the capacity to address the challenges of this new paradigm. Hence, understanding the importance and applicability of AI in agriculture and food sector could be vital in the journey towards achieving global food security. This review focuses on the AI applications in relation to four pillars of food security (food availability, food accessibility, food utilization and stability) as defined by FAO, in detail. The AI technologies are being applied worldwide in all four pillars of food security even though it has been one of the slower adopted technologies compared to the rest. Nevertheless, it warrants exploring the capabilities of AI and their current impact on the food systems. It is eminent that AI technology has a key role to play in the future agriculture sector. The worldwide AI in agriculture market is expected to reach USD 2,075 million by 2024. Present article reveals how AI technologies could benefit global agriculture and food sector, and examines the ways by which AI can address the prominent issues in Sri Lankan agriculture sector such as labor scarcity, misuse of agrochemicals and inefficient food value chains. Though there are still many challenges and gaps to be addressed at research, policy, administrative and farmer levels, the immense potential of this novel technology should be exploited fast in the journey towards global food security.

Aims: To evaluate the technical efficiency (TE) in selected agricultural sub-sectors and to propose possible policy interventions to the government with the aim of reducing the poverty of farmers in the developing world. Study design: A meta-analysis based on empirical studies conducted by various scientists throughout the developing world. Methodology: Research articles for the meta-analysis were selected using a thorough screening process based on the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) concept. Selected 94 articles were sub-divided in to three main agriculture sub-sectors for detailed analysis; (a) paddy, other field crops-OFC and vegetables, (b) fruits, and (c) livestock. Mean TE of each crop or livestock type was calculated by averaging the TE values for a particular crop or livestock type across different studies included in this study. Results: TE data presented in the original articles showed a considerable dispersion within a given study. The highest mean TE was recorded in B-onion (0.83±0.15) whereas the lowest was recorded in maize (0.703±0.09) and in soybean (0.705±0.13). The TE of chili cultivation was 0.78 with the greatest variability (standard error of mean [SEM] 0.19) among the crops considered, which signifies the unpredictable nature of the chili cultivation. Mango was found to be the least technically efficient crop among the studied, with a mean TE of 0.596±0.11. Dairy, poultry and aquaculture farming operations were found to be highly technically efficient having mean TE values of 0.80±0.16, 0.89±0.02 and 0.88±0.08 respectively. Conclusion: Findings of this study will lead to several key policy implications including, improvement of the socioeconomic characteristics of farmers, implementation of farmer field schools (FFS) and establishment of a cautious and gradual strategy for expansion of the rural financial institutions.