The quality and safety of food is an increasing concern for worldwide business. Non-destructive methods (NDM), as a means of assessment and instrumentation have created an esteemed value in sciences, especially in food industries. Currently, NDM are useful because they allow the simultaneous measurement of chemical and physical data from food without destruction of the substance. Additionally, NDM can obtain both quantitative and qualitative data at the same time without separate analyses. Recently, many studies on non-destructive detection measurements of agro-food products and final quality assessment of foods were reported. As a general statement, the future of using NDM for assessing the quality of food and agricultural products is bright; and it is possible to come up with interesting findings through development of more efficient and precise imaging systems like the machine vision technique. The present review aims to discuss the application of different non-destructive methods (NDM) for food quality and safety evaluation.

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 release of nanoparticles into the environment is inevitable, which has raised global environmental concern. Melatonin is involved in various stress responses in plants. The present study investigated the effects of melatonin on photosynthetic carbon (C) assimilation and plant growth in nano-ZnO stressed plants. It was found that melatonin improved the photosynthetic C assimilation in nano-ZnO stressed wheat plants, mainly due to the enhanced photosynthetic energy transport efficiency, higher chlorophyll concentration and higher activities of Rubisco and ATPases. In addition, melatonin enhanced the activities of antioxidant enzymes to protect the photosynthetic electron transport system in wheat leaves against the oxidative burst caused by nano-ZnO stress. These results suggest that melatonin could improve the tolerance of wheat plants to nano-ZnO stress.

This study was conducted to investigate the effects of various irrigation water (W) and nitrogen (N) levels on growth, root-shoot morphology, yield, and irrigation water use efficiency of greenhouse tomatoes in spring–summer and fall–winter. The experiment consisted of three irrigation water levels (W: 100% of crop evapotranspiration (ETc), 80%, and 60% of full irrigation) and three N application levels (N: 100%, 75%, and 50% of the standard nitrogen concentration in Hoagland’s solution treatments equivalent to 15, 11.25, 7.5 mM). All the growth parameters of tomato significantly decreased (p < 0.05) with the decrease in the amount of irrigation and nitrogen application. Results depicted that a slight decrease in irrigation and an increase in N supply improved average root diameter, total root length, and root surface area, while the interaction was observed non-significant at average diameter of roots. Compared to the control, W₈₀ N₁₀₀ was statistically non-significant in photosynthesis and stomatal conductance. The W₈₀ N₁₀₀ resulted in a yield decrease of 2.90% and 8.75% but increased irrigation water use efficiency (IWUE) by 21.40% and 14.06%. Among interactions, the reduction in a single factor at W₈₀ N₁₀₀ and W₁₀₀ N₇₅ compensated the growth and yield. Hence, W₈₀ N₁₀₀ was found to be optimal regarding yield and IWUE, with 80% of irrigation water and 15 mM of N fertilization for soilless tomato production in greenhouses.

Root morphology and its components’ behavior could show a considerable response under multiple water application points per plant to help the ultimate effect of fruit yield and fruit quality. In this study, a comparison of a single emitter per plant was made with two, three, and four emitters per plant under drip irrigation and two irrigation levels (full irrigation 100% and deficit irrigation 75% of crop evapotranspiration) to investigate their effects on physiological parameters, root, yield, and their associated components for potted cherry tomato under greenhouse conditions in Jiangsu-China. The experimental results showed that the plants cultivated in the spring-summer planting season showed significantly higher results than the fall-winter planting season due to low temperatures in the fall-winter planting season. However, the response root length, root average diameter, root dry mass, leaf area index, photosynthetic rate, transpiration rate, fruit unit fresh weight, the number of fruits, and pH were increased by multiple emitters per plant over a single emitter per plant, but total soluble solids decreased. Besides, a decreasing trend was observed by deficit irrigation for both planting seasons, and vice versa for the case for tomato total soluble solids. Due to an increase in measured parameters for multiple emitters per plant over a single emitter per plant, the yield, water use efficiency, and water use efficiency biomass significantly increased by 18.1%, 17.6%, and 15.1%, respectively. The deficit irrigation caused a decrease in the yield of 5% and an increase in water use efficiency and water use efficiency biomass of 21.4% and 22.9%, respectively. Two, three, and four emitters per plant had no significant effects, and the obtained results were similar. Considering the root morphology, yield, water use efficiency, water use efficiency biomass, and fruit geometry and quality, two emitters per plant with deficit irrigation are recommended for potted cherry tomato under greenhouse conditions. The explanation for the increased biomass production of the plant, yield, and water use efficiency is that two emitters per plant (increased emitter density) reduced drought stress to the roots, causing increased root morphology and leaf area index and finally promoting the plant’s photosynthetic activity.