Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030—Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.

The objective of fully mechanizing olive harvesting has been pursued since the 1970s to cope with labor shortages and increasing production costs. Only in the last twenty years, after adopting super-intensive planting systems and developing appropriate straddle machines, a solution seems to have been found. The spread of super-intensive plantings, however, raises serious environmental and social concerns, mainly because of the small number of cultivars that are currently used (basically 2), compared to over 100 cultivars today cultivated on a large scale across the world. Olive growing, indeed, insists on over 11 million hectares. Despite its being located mostly in the Mediterranean countries, the numerous olive growing districts are characterized by deep differences in climate and soil and in the frequency and nature of environmental stress. To date, the olive has coped with biotic and abiotic stress thanks to the great cultivar diversity. Pending that new technologies supporting plant breeding will provide a wider number of cultivars suitable for super-intensive systems, in the short term, new growing models must be developed. New olive orchards will need to exploit cultivars currently present in various olive-growing areas and favor increasing productions that are environmentally, socially, and economically sustainable. As in fruit growing, we should focus on “pedestrian olive orchards”, based on trees with small canopies and whose top can be easily reached by people from the ground and by machines (from the side of the top) that can carry out, in a targeted way, pesticide treatments, pruning and harvesting.

The phenolic profiles of six varieties of avocado (Persea americana) grown in Sicily were investigated. The ultra-high-performance liquid chromatography-heated electrospray-mass spectrometry method was developed to determine qualitative and quantitative changes in fruits at two different ripening stage. Nineteen individual phenolic compounds were detected in avocado pulp extracts. Gallic acid, sinapinic acid, vanillin, p-coumaric acid, and gentisic acid were present only in ripe fruits. On the contrary, epicatechin decreased with fruit ripening, whereas protocatechuic acid, 4-hydroxybenzoic acid, chlorogenic acid, and benzoic acid were relatively stable or exhibited erratic changes with fruit ripening. The different avocado cultivars exhibited different phenolic profiles and total quantities and it was found that, among the tested cultivars, “Orotawa” avocados may provide the highest nutritional and health contribution to human diet. The qualitative and quantitative differences among cultivars and maturation stages by multivariate analysis allowed for the individuation of a set of phenolic compounds that have a great potential in the discrimination and identification of different genetic groups.