Agriculture 5.0 (Ag5.0) represents a groundbreaking shift in agricultural practices, addressing the global food security challenge by integrating cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), robotics, and big data analytics. To adopt the transition to Ag5.0, this paper comprehensively reviews the role of AI, machine learning (ML) and other emerging technologies to overcome current and future crop management challenges. Crop management has progressed significantly from early agricultural methods to the advanced capabilities of Ag5.0, marking a notable leap in precision agriculture. Emerging technologies such as collaborative robots, 6G, digital twins, the Internet of Things (IoT), blockchain, cloud computing, and quantum technologies are central to this evolution. The paper also highlights how machine learning and modern agricultural tools are improving the way we perceive, analyze, and manage crop growth. Additionally, it explores real-world case studies showcasing the application of machine learning and deep learning in crop monitoring. Innovations in smart sensors, AI-based robotics, and advanced communication systems are driving the next phase of agricultural digitalization and decision-making. The paper addresses the opportunities and challenges that come with adopting Ag5.0, emphasizing the transformative potential of these technologies in improving agricultural productivity and tackling global food security issues. Finally, as Agriculture 5.0 is the future of agriculture, we highlight future trends and research needs such as multidisciplinary approaches, regional adaptation, and advancements in AI and robotics. Ag5.0 represents a paradigm shift towards precision crop management, fostering sustainable, data-driven farming systems that optimize productivity while minimizing environmental impact.

The Hayani cultivar of date palms is the most grown type in North Sinai, Egypt, and is widely sold in markets. However, its fruit quality is not good, and it is facing competition from imported cultivar like Barhi. As a result, it is important to improve the quality of Hayani fruits in order to keep them popular and maintain this local cultivar. In this study, the effects of spraying Hayani date palm fruits with chitosan, arginine, and galangal extract at 100 ppm, 10 mg/l, and 25 mg/l respectively either individually or mixed, on fruit yield and quality were investigated. Results indicated that fruit yield, yield components, and chemical and biochemical characteristics of Hayani dates fruits were improved when spraying arginine at 100 ppm. In general, the spray mixture of arginine at 100 ppm, chitosan at 10 ml/L and galangal extract at 25 ml/L was mostly more effective than the individual application of each of them, as evidenced by the results of all parameters evaluated. This resulted in an increase in bunch weight, fruit weight per bunch, fruit number, flesh net weight, fruit dimensions, fruit volume, total soluble solids (TSS), DPPH scavenging activity, total phenolic content, and total vitamin C. Additionally, fruit color was darker red with this treatment application than the control treatment that shown as pale red color. In contrast, the mixture of arginine with chitosan and galangal extract treatment produced lower tannin content in the fruits.

The study was conducted to determine the best time for harvesting the fruits of four olive cultivars in North Sinai- Egypt, to help olive farmers obtain the highest oil yield and quality under current climate fluctuations. This experiment involved fruit harvesting of four commercial olive cultivars (Picual, Manzanillo, Koroneiki, and Chemlali) on four different dates, (5th October, 19th October, 2nd November, and 16th November). The results showed that the maturity index (MI) varied between cultivars. Manzanillo fruits mature more than 15- 30 days earlier than other cultivars. Late harvesting dates recorded the highest maturity index compared to early harvesting dates. Oil concentration was cultivar-dependent but has been affected by climate change. The olive cultivars were arranged in descending order based on their oil content as follows: Chemlali, Koroneiki, Picual, and Manzanillo. The oil content also increased with increasing fruit maturity and then decreased significantly with increasing maturity index. Phenol, chlorophyll, and carotene content decreased as the ripening stage progressed, while the acidity and peroxide content increased. The most suitable harvesting dates were determined based on the maturity index, oil content, and total phenolic content. We recommended that the best time to harvest was 19th October for Manzanillo which can be two weeks early in hot seasons, in the second November for Picual, and Chamalali, and from 2 to 16 November for Koroneki.