How smart sensors, satellite and aerial imaging platforms and cloud-based data infrastructures play a vital role in the development of crop health monitoring and predictive analytics is discussed, including the heterogeneity of data, false positives, and the interpretability of the AI models.
Abstract
Sustainable agriculture is critical towards providing food security in the world and at the same time dealing with environmental degradation as well as shortages of resources. The loss of soil biodiversity, an increased resistance of pests and pathogens, and general decrease in crop yield have been contributed by traditional farming methods especially excessive use of agrochemicals. The deployment of cloud computing and remote sensing through the combination of artificial intelligence (AI) has allowed the expansion and scope of smart agriculture to reach cost-effective and responsive solutions to various farming settings. Nevertheless, the existing literature does not usually consider the potential of these technologies combined and does not provide a comprehensive approach to the implementation of these technologies in the synergy of their application. This review carries out extensive discussion of AI-based sustainable crop production by combining cloud computing and remote sensors. It discusses how smart sensors, satellite and aerial imaging platforms and cloud-based data infrastructures play a vital role in the development of crop health monitoring and predictive analytics. The major issues such as the heterogeneity of data, false positives, and the interpretability of the AI models are discussed critically. The areas of future research are also proposed to aid in creating multi-modal, robust, and interpretable AI systems that can propel sustainable, intelligent, and resilient agricultural ecosystems.
Agricultural water scarcity poses an escalating threat to global food production, driven by population growth, climate variability, and the inherent inefficiencies of conventional irrigation systems. This paper analyzes the transformational capacity of smart technologies like the Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML), and Remote Sensing (RS) integrated with Geographic Information Systems (GIS) for sustainable agricultural water management. By synthesizing recent empirical studies, the paper analyzes the integrated effect of sensor-based monitoring, predictive analytics, automated irrigation control, and satellite and drone surveillance on water-use efficiency, irrigation optimization, and climate-resilient agriculture. Key findings indicate that IoT-based smart irrigation systems reduce water consumption by 20–55% and improve crop yields by 10-30% under optimized conditions, particularly when used with AI-driven decision-support platforms. RS and GIS technologies enable scalable, cost-effective estimation of evapotranspiration and crop water stress across field to regional scales. Despite these demonstrated benefits, substantial barriers to adoption persist, including high implementation costs, inadequate rural digital infrastructure, limited technical literacy among smallholder farmers, and insufficient policy frameworks. This review emphasized the need for supportive policies, cost-reduction strategies, and capacity-building initiatives to bridge the gap between technological innovation and field-level implementation. Overall, smart water management technologies offered a promising pathway toward sustainable agriculture, improved food security, and responsible water stewardship in an increasingly water-scarce world.
India, like many other developing countries, has agriculture as one of its vital areas of socio-economic growth, and the traditional methods of farming are under great pressure because of the rise in population, weather changes, soil erosion and the scarcity of resources. This encourages the use of precision agriculture that incorporates the use of advanced technologies to optimize the production of crops and control of soils. Recent studies have highlighted the importance of AI, machine learning, IoT, and cloud computing in changing the way agriculture is done by allowing real-time monitoring of soils and making decisions based on data. The use of AI-based soil analysis and crop recommending systems with sensor networks to measure soil moisture, temperature, pH, nutrient, and pollutant levels have attracted a substantial amount of literature. These research illustrate better prediction of soil health, better irrigation timing and better recommendations of fertilizers. Nonetheless, most systems suffer the challenges of high computational cost, small field validation, reliance on big data and smallholder farming unscalability. Taken together, these literature sources point to an empty space in the research that would allow creating cost-effective, scalable, and user-friendly soil monitoring systems that can combine various parameters of soils into a single AI-based platform. The proposed study will help to fill this gap by creating an AI-based soil fertility assessment and crop recommendation system powered by sensor networks using IoT, cloud analytics, and deep learning models to improve the sustainability of agricultural productivity and soil health management.
MS. Priti PRATIK DHIMMAR· Journal of Global Economics· 0 citations
The growing global food production demand along with rapidly accelerating consequences from climate change, continued environmental degradation, and scarce natural resources have resulted in a great urgency for intelligent agricultural management systems. Conventional agricultural systems rely heavily on regular field phonological check-ups and seasonal environmental monitoring, which do not deliver timely or accurate information for informed sustainable decision-making. ABSTRACT Recent developments in artificial intelligence (AI), remote sensing technologies, unscrewed aerial vehicles (UAVs), satellite imaging, Internet of Things(IoT) devices, cloud computing, and geospatial analytics have opened wider scope for the transition of agricultural production as well as environmental monitoring to extremely intelligent data-driven operations. Through the integration of these emerging technologies, it is possible to monitor crop health status and soil properties together with weather conditions temperature and transfer moisture, water resources, and vegetation dynamics, biodiversity, changes in ecosystems over large areas at higher spatial-temporal resolution. This study presents a framework for integrated AI and remote sensing tools, intended to assist sustainable agriculture by means of intelligent data acquisition/sensing, preprocessing techniques, feature extraction methodologies, predictive analytics algorithms, and decision-support systems. In this paper, a framework is proposed based on multispectral satellite imagery and data from hyperspectral sensing techniques (UAVs), ground sensor networks, machine learning algorithms/deep-learn architectures; GIS & cloud-based analytical platforms. This aims to generate precise recommendations in small time scales for precision farming as well as for the protection of environmental habitats. The new framework would help with early detection of crop diseases, optimal irrigation scheduling, and prediction of agricultural yield or production capacity for example the best planting areas, land use change monitoring and carbon sequestration estimation as well as drought severity assessment & biodiversity conservation efforts. The review integrates the conceptual frameworks underlying AI and precision agriculture, remote sensing applications of electronic data in environmental analytics, intelligent decision-support systems, and current technological limitations and research gaps. In addition, an innovative research methodology is conceived to assess the performance of multidimensional indicators (prediction accuracy & computational efficiency), and new yardsticks for measuring scalability along with environmental impact/recyclability will be utilized. Comparative analysis shows that AI for multisource remote sensing contains increased agricultural productivity, environmental sustainability, as well as operational savings in terms of lower chemical usage and climate-resilient practices. The results suggest that hybrid AI models combining convolutional neural networks (CNNs), recurrent neural network (RNN) in landscape features, transformer architectures in area plots with ensemble learning and explainable artificial intelligence for interpreting top items outperform traditional statistical methods based on heterogeneous geospatial datasets. The ability to enable cloud-based big data infrastructures now also permits near real-time monitoring over large agricultural areas whilst enhancing compatibility between satellite systems, inter-networked devices and government decision-support platforms. The proposed framework advances sustainable digital agriculture by offering a scalable, automated intelligent solution that can be adopted for emerging agricultural and environmental challenges in varying climates and geographies.
Muhammad Wali Khan, Nith Ya· International Journal of Agr...· 0 citations
The sphere of agriculture is currently experiencing an enormous change fueled by the incorporation of the Artificial Intelligence (AI), Internet of Things (IoT), big data analytics, and new sensing technologies. The challenges to farming by the traditional practices usually include; poor use of resources, unpredictable weather, pests, and poor production. AI-based smart farming will provide data-driven, demonstration-free, and predictive solutions that will improve crop yielding, optimize resource use and enable sustainable and agricultural development. Within this paper, a detailed analysis of AI-based smart farming technologies to improve the crop condition is provided, including smart decision-making, precision agriculture, and real-time detection. The suggested framework is an integration of machine learning algorithms, computer vision, remote sensing, and internet of things with devices that will be used to touch soil, weather condition, and crop growth phases and pests. The prediction of yields, detection of diseases and the optimization of irrigation are reviewed using various AI methodologies including supervised learning, deep learning, and reinforcement learning. A comparative analysis shows that AI-based methods outperform the traditional farming methods in the aspects of productivity, economic efficiency, and environmental sustainability. The implementation challenges and scalability issues, as well as research directions are also addressed in the study. The results show that AI-combinations with smart farming can transform the agriculture sector as the application can lead to better quality of crops, higher yield and sustaining food security the world over due to climatic change and population explosion.
Thomas Fischer, Anna Schmidt· International Journal of Mod...· 0 citations
The agricultural practice is changing at a fast pace with farmers experiencing increasing problems in terms of climate variability, soil degradation, pest outbreaks, and ineffective utilization of inputs. These problems usually result in decreased productivity and increase in operation costs particularly among small and medium farmers who do not easily access modern tools. The proposed study is a Smart Farming System, which incorporates the use of artificial intelligence, machine learning, and IoT-based sensing technologies to aid data-driven agriculture decision-making. The system collects real-time data on the moisture of soil, nutrient level, weather, and crop health as a result of distributed sensors, drones, and remote imagery. This data is analyzed using machine learning models to give personalized suggestions on irrigation, fertilization and pest management, and image processing CNN-based image processing detects the early disease indicators. Predictive analytics are also useful in estimating crop yield and predicting possible risks. The proposed system is meant to improve the efficiency of the farm, decrease the wastage of the resources, and facilitate the sustainable farming techniques. The solution could be used to close the technology divide between conventional operations and the modern precision agriculture through the provision of a scalable and user-friendly platform.
M. B, Mythreyan A, D. P et al.· 2026 4th International Confe...· 0 citations
ABSTRACT
Agriculture remains the backbone of many economies worldwide, yet farmers continue to face challenges related to climate variability, water scarcity, soil degradation, pest infestations, and inefficient resource utilization. Traditional farming methods often rely on manual observation and experience-based decision-making, which may lead to reduced productivity and increased operational costs. The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) has emerged as a transformative solution for modern agriculture by enabling real-time monitoring, intelligent data analysis, and automated decision-making. This paper proposes a Smart Farming System that combines IoT sensors, wireless communication technologies, cloud computing, and AI-based predictive models to optimize agricultural practices. The proposed framework continuously monitors environmental conditions such as soil moisture, temperature, humidity, light intensity, and crop health. AI algorithms analyze the collected data to predict irrigation requirements, detect diseases, estimate crop yield, and recommend suitable farming actions. The system aims to improve agricultural productivity, reduce resource wastage, and promote sustainable farming practices. Experimental results demonstrate significant improvements in water efficiency, crop yield prediction accuracy, and overall farm management performance. The proposed solution provides a scalable and cost-effective approach toward the development of intelligent agriculture systems capable of meeting future food security demands.
Keywords— Smart Farming, Internet of Things (IoT), Artificial Intelligence (AI), Precision Agriculture, Crop Monitoring, Machine Learning, Sustainable Agriculture, Smart Irrigation.
N. Swaroop, Gudesela Madhu Kumar, D. P. Balaji· International Scientific Jou...· 0 citations
