Technological advancements have led to an increased use of the internet of things (IoT) to enhance the resource use efficiency, productivity, and cost-effectiveness of agricultural production systems, particularly under the current scenario of climate change. Increasing world population, climate variations, and propelling demand for the food are the hot discussions these days. Keeping in view the importance of the abovementioned issues, this manuscript summarizes the modern approaches of IoT and smart techniques to aid sustainable crop production. The study also demonstrates the benefits of using modern IoT approaches and smart techniques in the establishment of smart- and resource-use-efficient farming systems. Modern technology not only aids in sustaining productivity under limited resources, but also can help in observing climatic variations, monitoring soil nutrients, water dynamics, supporting data management in farming systems, and assisting in insect, pest, and disease management. Various type of sensors and computer tools can be utilized in data recording and management of cropping systems, which ensure an opportunity for timely decisions. Digital tools and camera-assisted cropping systems can aid producers to monitor their crops remotely. IoT and smart farming techniques can help to simulate and predict the yield production under forecasted climatic conditions, and thus assist in decision making for various crop management practices, including irrigation, fertilizer, insecticide, and weedicide applications. We found that various neural networks and simulation models could aid in yield prediction for better decision support with an average simulation accuracy of up to 92%. Different numerical models and smart irrigation tools help to save energy use by reducing it up to 8%, whereas advanced irrigation helped in reducing the cost by 25.34% as compared to soil-moisture-based irrigation system. Several leaf diseases on various crops can be managed by using image processing techniques using a genetic algorithm with 90% precision accuracy. Establishment of indoor vertical farming systems worldwide, especially in the countries either lacking the supply of sufficient water for the crops or suffering an intense urbanization, is ultimately helping to increase yield as well as enhancing the metabolite profile of the plants. Hence, employing the advanced tools, a modern and smart agricultural farming system could be used to stabilize and enhance crop productivity by improving resource use efficiency of applied resources i.e., irrigation water and fertilizers.

Rapid population growth, rising food demand, and climate change have created significant challenges to meet the water demands for agriculture. Effective irrigation water management is essential to address the world’s water crisis. The transition from conventional, frequently ineffective gravity-driven irrigations to contemporary, pressure-driven precision irrigation methods are explored in this article, addressing the difficulties associated with water-intensive irrigation, the possibility of updating conventional techniques, and the developments in smart and precision irrigation technologies. This study comprehensively analyses published literature of 150 articles from the year 2005 to 2024, based on titles, abstract, and conclusions that contain keywords such as precision irrigation scheduling, water-saving technologies, and smart irrigation systems, in addition to providing potential solutions to achieve sustainable development goals and smart agricultural production systems. Moreover, it explores the fundamentals and processes of smart irrigation, such as open- and closed-loop control, precision monitoring and control systems, and smart monitoring methods based on soil data, plant water status, weather data, remote sensing, and participatory irrigation management. Likewise, to emphasize the potential of these technologies for a more sustainable agricultural future, several smart techniques, including IoT, wireless sensor networks, deep learning, and fuzzy logic, and their effects on crop performance and water conservation across various crops are discussed. The review concludes by summarizing the limitations and challenges of implementing precision irrigation systems and AI in agriculture along with highlighting the relationship of adopting precision irrigation and ultimately achieving various sustainable development goals (SDGs).

The increasing population, its requirements for food, and the environmental impact of the excessive use of inputs make crop production a pressing challenge. Integrated nutrient management (INM) has emerged as a critical solution by maximizing nutrient availability and utilization for crops and vegetables. This review paper highlights the potential benefits of INM for various vegetables and field crops and explores the conceptual strategies, components, and principles underlying this approach. Studies have shown that a wide range of vegetables and field crops benefit from INM, in terms of increased yield and improvements in yield attributes, nutrient contents and uptake, growth parameters, and various physiological and biochemical characteristics. This paper discusses biostimulants, their categories, and their impact on plant propagation, growth, photosynthesis, seed germination, fruit set, and quality. Additionally, this review explores modern sustainable soilless production techniques such as hydroponics, aeroponics, and aquaponics. These cultivation methods highlight the advancements of controlled-environment agriculture (CEA) and its contribution to nutrient management, food security and minimizing the environmental footprint. The review concludes by proposing methods and fostering discussions on INM’s future development, while acknowledging the challenges associated with its adoption. Finally, this review emphasizes the substantial evidence supporting INM as a novel and ecologically sound strategy for achieving sustainable agricultural production worldwide.

Drought is considered as one of the critical abiotic stresses affecting the growth and productivity of upland rice. Advanced and rapid identification of drought-tolerant high-yielding genotypes in comparison to conventional rice breeding trials and assessments can play a decisive role in tackling climate-change-associated drought events. This study has endeavored to explore the potential of the CERES–Rice model as a decision support tool (DST) in the identification of drought-tolerant high-yielding upland rice genotypes. Two experiments mentioned as potential experiment (1) for model calibration under optimum conditions and an experiment for yield assessment (2) with three irrigation treatments, (i) a control (100% field capacity [FC]), (ii) moderate stress (70% FC), and (iii) severe stress (50 % FC), were conducted. The results from the yield assessment experiment indicated that the grain yield of the studied genotypes decreased by 24–62% under moderate stress and by 43–78% under severe stress as compared to the control. The values for the drought susceptibility index (DSI) ranged 0.54–1.38 for moderate stress and 0.68–1.23 for severe stress treatment. Based on the DSI and relative yield, genotypes Khao^/Sai, Dawk Kham, Dawk Pa–yawm, Goo Meuang Luang, and Mai Tahk under moderate stress and Dawk Kha, Khao^/Sai, Nual Hawm, Dawk Pa–yawm, and Bow Leb Nahag under severe stress were among the top five drought-tolerant genotypes as well as high-yielding genotypes. The model accurately simulated grain yield under different irrigation treatments with normalized root mean square error < 10%. An inverse relationship between simulated drought stress indices and grain yield was observed in the regression analysis. Simulated stress indices and water use efficiency (WUE) under different irrigation treatments revealed that the identified drought-tolerant high-yielding genotypes had lower values for stress indices and an increasing trend in their WUE indicating that the model was able to aid in decision support for identifying drought-tolerant genotypes. Simulating the drought stress indices could assist in predicting the response of a genotype under drought stress and the final yield at harvest. The results support the idea that the model could be used as a DST in the identification of drought-tolerant high-yielding genotypes in stressed as well as non-stressed conditions, thus assisting in the genotypic selection process in rice crop breeding programs.

Light is a crucial mediator in plants of growth and development, secondary metabolism, and signaling of potentially beneficial phytochemicals. The present study investigated three indoor supplemental light treatments, HPS, LED, and LED+IR, applied during tomato cultivation, in which non-destructive and destructive analyses were performed at two time points to record the physiological responses and effects of these artificial lights on the growth and quality of the produce. An infrared thermometer and thermal cameras were used throughout the experiment to record temperature changes under each growing condition. LED+IR supplemented light showed a decrease in photosynthetic pigments, such as chlorophyll a, b, and carotenoids but induced an increased number of flowers, heavier tomato fruits, enhanced anthocyanins, phenolic index, lipid peroxidation, titratable acidity, and reduced nitrate accumulation. Moreover, higher generated temperatures under LED+IR helped tomato plants to reduce white fly infestation, but suppressed plant height. HPS light performed better than both LED and LED+IR in terms of total sugar accumulation, total carotenoids, water content, plant height, and leaf lipid peroxidation at harvest. Lycopene, ß-carotene, brix index were remarkable under HPS lighting but under the same conditions the number of whiteflies, however, was the highest in HPS among all the tested light treatments. In terms of fruit color analysis, maximum redness (a*), reduced hue angle, and chroma were observed under LED+IR while LED lighting in brightness (L*), and HPS in yellowness (b*) were prominent. However, LED in particular was insufficiently effective in acquiring any possible physiological and qualitative characteristics of tomato plants and fruits for the observed time span of the experiment. Hence, LED+IR has been shown to boost the accumulation of bioactive chemicals, improve fruit quality, promote more rapid and early flowering in tomato plants, and can serve as an efficient replacement for traditional indoor illumination.

Additional artificial ultraviolet-B irradiation (UV-B) is a substitute for the natural UV-B radiation, which is believed to modulate the antioxidants production in plants against the abiotic stress. The present research was formulated by using an indoor UV-B chamber, capable of irradiating UV-B at single wavelength of 315 nm on spearmint. In vitro destructive analysis revealed the reduction in accumulation of chlorophyll a and b as well as carotenoids. However, an elevated levels of phenolic index and total anthocyanins were recorded for both 43.2 KJ/m² and 86.4 KJ/m² UV-B doses. TBARS assay was carried out to record the lipid peroxidation in the spearmint and likewise, an enhanced secondary metabolites production, an increased lipid peroxidation was seen with the successive increased in the UV–B doses. A remarkable accumulation in the total sugars, reducing sugars and sucrose were also noticed. In vivo analysis revealed a decrease in chlorophyll estimated values as well as an increase of flavanols. Overall leaf fluorescence performance index was improved under both UV-B doses as well as the maximum quantum efficiency of photosystem II. Decreases in the total yield, plant height and length of internode was observed for both UV-B doses however, an enhanced number of stems was recorded for the 43.2 KJ/m² UV-B dose. The results of present study not only provided a clear indication of the potential of UV-B in modifying the phytochemicals accumulation in plants but also opened a room for investigating various indoor UV-B doses for future studies of plants and their interaction towards UV-B. Graphical