Sustainable agricultural development presents a crucial solution to address rapid population growth, facilitated using ICT in precision agriculture. This approach has yielded innovative techniques that enhance agricultural productivity, efficiency, and regulation while concurrently preserving the environment [2]. To ensure long-term agricultural production, modern technologies must be used in the agricultural field, like blockchain [13,14], the IoT [15], and (AI) [16]. The most promising strategy for solving these problems is data-driven agriculture using these technologies. (IoT) assists in data collection at all stages of agricultural production and supply chain management [17].

The internet now plays a crucial role across many industries. Within the agricultural domain, a suggested approach that involves monitoring agricultural fields utilizing the IoT is utilized. This approach employs sensors that analyze diverse parameters within the agricultural domain, leveraging wireless sensor network technology [18]. “IoT” refers to the connection of devices and tools, such as sensors, to the internet, allowing them to transfer data. The IoT is a huge network with an enormous number of objects connected to a global information infrastructure. The number of connected devices has increased many folds, giving rise to the scalability issue in the IoT. For proper communication to take place, a unique sender and receiver must be recognized, along with the identification of an appropriate path or channel [19]. In agriculture, the use of IoT technology has the potential to revolutionize the way farms operate and increase the efficiency and productivity of the industry. Another application of IoT in agriculture is the use of precision farming techniques, which involve using sensors and other technologies to gather data and make precise and timely adjustments to various aspects of farming operations. For example, GPS-guided machinery can be used to precisely apply fertilizers and pesticides, reducing the number of resources used and minimizing waste. Even though, due to the necessity for skills in interacting with sensors, the internet cloud, and end-user apps, farmers face difficulties adopting smart farming and IoT technology [20].

The Internet of Things has numerous applications in the Digital Agriculture field, including detection of plant physiological status, nutrient replenishment recommendations, water requirements, and so on [18,21]. The (IoT) offers significant benefits for agriculture and sustainable crop production, including the ability to anticipate and prepare for potential disturbances that may originate from remote locations, such as insect pest invasions and monitoring soil nutrients, water dynamics, and pest management. The other advantages of using IoT technology in farming systems are that the ability of using in yield prediction, energy and cost savings, and the establishment of indoor vertical farming systems by utilizing the information from the established IoT station there [20]. Regarding ecological aspirations, the Internet of Everything (IoE) is receiving an increasing amount of attention [22]. This increases the effectiveness of protection and hence lowers costs. Big data can be utilized for different spatiotemporal applications, such as connecting remote IoT equipment to cloud-based computing capabilities [23,24]. The use of IoT with big data analysis techniques is the basis for new decision making tools.

In agriculture, “big data” refers to the massive amount of data generated by agricultural activity and measurement. Processing and managing large amounts of data is a difficult undertaking when using standard approaches and systems [7]. Big data have a big chance in agriculture for solving various farming difficulties and, as a result, increasing agricultural production quality and quantity. Big data analytics can be used to anticipate agricultural harvesting time, soil quality, crop protection, and irrigation requirements. Furthermore, big data empowers agricultural practitioners and related industries to gain information about different factors that impede agricultural production and make efficient decisions in daily farming [2]. While it is unclear whether agriculture practitioners’ information will be replaced by algorithms, big data applications are likely to revolutionize how agriculture farms are managed and operated [25,26].

The collection of data from multiple sources is a critical component of achieving predictive decision making capabilities in precision agriculture. Weather conditions play a pivotal role in determining the productivity and management of agricultural systems, emphasizing the importance of accurate area-based weather forecasting for the future of precision crop production and with further data collection from various sources would allow to monitor and optimize crop growth conditions [27]. Precision agriculture datasets typically comprise a diverse range of data related to crops, soil, and nutrients, atmospheric data, technological data such as GIS data, GPS data, and data from trucks and the Variable Rate Fertilizer (VRT) system [7,28,29,30].

Big data analysis is a methodical strategy that applies cutting-edge analytic tools to big data sets. This entails the integration of two technical components, namely extensive data sets and an array of analytics tool categories such as data mining, statistics, AI, predictive analytics, and natural language processing (NLP), among others. The analysis of such data is accomplished through the utilization of big data mining techniques. In the realm of smart agriculture, the feasibility study is significantly improved by the application of IoT and big data analytics in the cloud [2,18]. 

Cloud computing is a method that allows for the sharing of resources at a low cost. Cloud computing service providers make these services available at an economical cost. The storage of agricultural data has been facilitated through the utilization of cloud computing. In the agriculture sector, cloud computing is employed in conjunction with IoT technology [33]. According to Neményi et al., 2022 [34], the combination of artificial intelligence and cloud computing constitutes a comprehensive support system for the IoT. It requires combining data from intelligent wireless sensors, such as unmanned aerial vehicles (UAVs) [35] and satellites, data acquisition systems on agricultural machinery and crop production equipment, and robots such as unmanned ground vehicles (UGVs), with precise positioning systems (such as navigation systems, real-time kinematics (RTK), and Lidar). The Wireless Sensor Network (WSN) is a wireless network made up of geographically distributed sensor stations.

Sensors → MCU → Nodes → Gateways → Clouds (server) [34].

Multisensor data fusion is a technology that facilitates the integration of data from multiple sources to create a comprehensive representation. Data fusion systems have found broad use in many different domains, including but not limited to sensor networks, robotics, video and image processing, and intelligent system design. The process of data fusion is described as a complex, multifaceted operation that involves automated detection, association, correlation, estimation, and gathering of data from multiple sources at various levels [36,37]. The practice of data fusion offers numerous benefits, primarily through improving the authenticity and availability of data. The utilization of data fusion strategies has been extensively applied in various sectors, including the food and chemical industries, with the aim of augmenting the analytical platforms’ overall performance and robustness [38,39,40].

In contemporary times, traditional yield modeling aimed at meeting the demands of sustainability and identifying yield drivers and factors restricting yield is increasingly being executed using AI. An expanding number of researchers are employing AI to model an extensive array of agricultural tasks. The technical literature sources reveal the application of diverse techniques for supporting precision agriculture decision making. AI presents a promising prospect for improving the world’s food security, including closing crop yield gaps, minimizing food waste, and mitigating resource utilization inefficiencies [42,43].

Counter propagation artificial neural network (CP-ANN), SKN, and XY-F models were used along with high-resolution soil and crop data such as spatial-temporal soil types and crop production effectiveness to predict classes of wheat yield productivity. The SKN network predicts the low category of yield output the best, with a proper classification percentage of 91.3% for both cross- and independent validation. Temperature and other climate effects have also been analyzed by AI [44,45,46]. These researches emphasize the importance of conventional yield modeling to be integrated with AI-driven methods in precision agriculture. Applications for (AI) like CP-ANN, SKN, and XY-F models show highly promising results, especially SKN with a 91.3% classification accuracy for low yield.

Precision agriculture involves the collection of real-time and historically generated data, which is structured or unstructured in nature. With the increasing adoption of precision agriculture practices, the amount of unstructured data generated has grown significantly. Consequently, current research efforts have focused on extracting meaningful insights and knowledge from these unstructured datasets [30]. High-spatiotemporal-resolution automated monitoring of the soil, plants, and atmosphere is an important factor in converting labor-intensive, experience-based decision making in agricultural production to an autonomous, data-driven way. Real-time field data might help growers make better management decisions, and researchers could use this information to find answers to important scientific problems [47].

The implementation of fully automated decision making systems (ADMS) is essential in precision agriculture. These systems are designed to monitor multiple chemical or physical parameters of soil and plants while simultaneously regulating them to maintain optimal conditions for each specific plant or soil type. Despite the feasibility of developing such systems using existing technologies, the implementation of ADMS encounters significant challenges. ADMS generally consists of four main sections: the sensing technique, sensor interfaces, the information transmission platform, and the data processing and control unit; the crucial role of precision agriculture in utilizing both historical and real-time data for well-informed decision making is highlighted by both studies. Despite its feasibility, both studies agree that deploying ADMS is challenging. They develop a comprehensive view of the changing precision agricultural scene, combining data-driven insights with the complexities of implementing automated systems technologies. 

There are now three fundamental approaches accessible for soil and plant sensing systems: satellite imaging, on-the-go sensors, and in situ soil and plant sensors. Satellite imaging is a way of monitoring soil and plant quality and fertility by acquiring and analyzing multi-spectral satellite images of the field of interest [49]. This technology is very costly and limited to particular countries.

The second, more financially practical way is to attach sensors to agricultural machinery, other agricultural equipment, or even drones, known as on-the-go sensors. The last approach entails the placement of soil and plant sensors across the area, which can share data in real time [50,51]. This is the only approach capable of providing continuous, real-time data without the need for human intervention. These sensors do, however, have some drawbacks, such as the need for protection from wild animals and the need to occasionally remove them from the field during crop cultivation and harvest to prevent damage from agricultural machinery. Depending on the period of removal, this may result in gaps and data loss for several hours or days.

Real-time soil nutrient sensing has a lot of potential for the future of agriculture because nitrogen leakage and groundwater pollution have developed into significant problems for society. Nitrate sensors can be used for a variety of purposes, such as monitoring nitrate concentrations in groundwater by mounting them to groundwater pumps or estimating nitrate leaching more precisely by installing them in leaching water collectors in fields [52]. The real-time measurement of soil nutrient content, particularly nitrogen, phosphorus, and potassium, is critical for fertilization. These detectors are still in the experimental stage [53].

Utilizing in situ and on-the-go sensing systems that could record significant data and soil parameters in a short period of time is one of the needs of precision farming and crop production. This representative data collection results in average cost-effectiveness, but by analyzing the data and taking additional measurements, it could also improve our current knowledge. Vis-NIR spectroscopy can be used to measure the soil’s pH, organic matter concentration, and moisture content [54]. In general, the more complicated the problem to be solved, the more data are required [55]. The analysis of big data sets is a way to identify relationships, patterns, and trends in the data [7]. Incorporating observations from farms and in situ sensing systems with current databases presents an opportunity not only to predict yields using traditional statistical techniques or DSS Decision Support Systems but also to explore the potential of machine learning (ML). This provides an avenue for more advanced and sophisticated yield prediction models to be developed [43]. For both in situ and on-the-go sensing systems the diverse and unstructured nature of the data produced by these sensors presents a significant challenge and requires the use of advanced tools for analysis, and because of the enormous volume of data, traditional storage and processing systems must be scalable. When dealing with data from different sensor types and manufacturers, interoperability concerns may occur, making integration more difficult. Furthermore, protecting data privacy and security is essential considering the sensitivity of agricultural information. To enable efficient and secure big data use in precision agriculture, it is necessary to create strong data management strategies, implement standardized protocols, and give security measures top priority.

The integration of microchips and broadband networks with farm equipment, crops, animals, machines, and products through an IoT platform is a technological advancement that offers real-time connectivity between devices [56]. The application of IoT is particularly relevant in sustainable agriculture, given the diversity of factors involved in the agricultural ecosystem, including biodiversity, stochastic weather patterns, and the interdependence of living and non-living systems. As a result, a comprehensive approach is necessary to address these complexities. In addition, IoT and AI can help reduce the effects of climate change and greenhouse gas emissions, further emphasizing the significance of these technologies in modern agriculture [57,58,59,60]. The research studies’ comparisons illustrate multiple approaches for soil and plant sensing systems in precision agriculture, each with unique benefits and limitations. Using multi-spectral images from satellite imaging to monitor soil and plant quality is highly expensive and limited to certain countries. Real-time data can be obtained through on-the-go sensors that are installed on machinery or drones, but these sensors must be protected from wildlife and occasionally removed during cultivation. Real-time soil nutrient sensing is currently in the experimental stage but is crucial for solving environmental challenges. With their diverse and unstructured data, in situ and on-the-go sensing systems provide a challenge that requires scalable storage and advanced analytical tools. For effective big data use in precision agriculture, both studies emphasize the value of robust data management, standardized protocols, and security measures.

Sensors are utilized at various stages of the farming process, from planting through the packaging of the final product. The farming process can be broadly classified into distinct categories, including planting, soil management, nutrient and water management, pest and disease management, yield harvesting, and post-harvest processing, all of which can benefit from the integration of advanced sensing technologies. 

Sensors serve a vital function in enabling the conversion of real-world signals into their digital representations within Ag-IoT systems. The integration of sensors in machine components has become increasingly prevalent, providing producers with insights into the systems’ dynamic loading and repetitive biotic and abiotic stresses. By collecting and comparing data from sensors positioned in the soil, crops, and animals, it is possible to identify technical improvements that prioritize criteria of ecological and economic sustainability, ultimately enhancing the soil-, plant-, and animal-friendly nature of precision agriculture [63].

The appropriate choice of sensors for an application is crucial for both inventors and users of IoT systems to ensure optimal sensor utilization. Technological advancements in sensors have significantly influenced the proliferation of the IoT. Key factors that must be considered when selecting sensors for IoT system development include low power consumption, information transfer compatibility between the computers and the sensor, precision, sensibility, repeatability, and durability [47].

When deployed on UAVs (drones, airplanes), UGVs (tiny, intelligent robots), and satellites, cameras with the Normalized Difference Vegetation Index (NDVI) can be utilized to estimate yield and using hyperspectral cameras and NDRE (normalized difference red edge) cameras can improve the estimates’ accuracy. Principal Component Analysis (PCA) and an Artificial Neural Network (ANN) can be used to perform color-based crop (fruit) maturity checks. Gloves with various sensors integrated, including touch pressure sensors, imaging, inertia measurement, location, and RFID (Radio Frequency Identification), are used to classify fruits and have been applied in fungal disease classification [77,78,79,80,81,82]. Sensors that move both vertically and horizontally within the soil or remain stationary in the ground have the potential to collect critical data. With the emergence of nanotechnology, the use of increasingly smaller sensors is becoming commonplace [83]. The implementation of such sensors, coupled with the resulting data collection, can significantly reduce losses during agricultural operations and increase their effectiveness. Furthermore, the data collected facilitates the establishment of clear classification conditions based on the inherent quality of the crop, thereby mitigating the risk of incorrect classification and associated losses [34].

One of the most significant advancements in infield monitoring over the past 10 years has been the replacement of wired-based complex systems with wireless sensor networks (WSN), complemented by effective power management techniques [92]. Sensor networks are used in precision agriculture to monitor field environmental conditions and other parameters. These sensors connect with one another, producing a network that collects environmental data collaboratively. WSNs are naturally specialized and have a simpler infrastructure, allowing them to transfer data easily and efficiently from the field to a remote user [93]. For the real-time monitoring of critical parameters like soil moisture and crop health, wireless sensor networks (WSN) have become essential in agriculture. They enable the effective use of water, fertilizer, and pesticides through optimizing resource management. WSNs improve data accuracy by efficiently distributing frequencies to reduce interference. Precise monitoring over large agricultural landscapes is made possible by considering signal intensity, bandwidth usage, and adaptation to changing transmission ranges. Energy efficiency is a top priority for WSNs for long-term deployment. They are essential for implementing precision agriculture and promoting sustainable farming practices due to their scalability and strong system models that adapt to various farm sizes and changing conditions.

Data transmission via wireless constitutes a crucial component of an IoT system, particularly in the agricultural domain. Designing an effective Ag-IoT system requires comprehending the contributing elements pertaining to radio frequency (RF) that impact signal strength, interference, system model, bandwidth, and transmission range. Moreover, a thorough understanding of the advantages and disadvantages of different wireless communication technologies is important for optimal device selection. Wireless IoT sensors can track environmental factors such as humidity, temperature, luminosity, and soil moisture in real time [94,95,96,97,98,99]. Agricultural data collected with sensors will be delivered in a variety of forms based on the required level of precision and resolution, necessitating the use of appropriate interfaces to ensure compatibility. In the realm of IoT-based intelligent farming, communication protocols are essential for covering both short-range and long-range distances on the land [100].

ZigBee, Bluetooth, NB-IoT (Narrowband LPWAN technology which can coexist in LTE or GSM under licensed frequency bands) [101], and Wi-Fi are used for short ranges, while LoRaWAN, Sigfox, LTE m (Long-Term Evolution for Machines) [102] and LPWAN protocols in mobile communication networks are utilized for long ranges. The LoRaWAN (Long Range Wide Area Network) is designed to function with low power consumption and is capable of transmitting sensor signals to a central server from up to 30–40 km away on level terrain [103]. In terms of communication technologies employed for node and gateway/base station interactions, half of the reviewed literature utilized LoRa/LoRaWAN. The selection of an appropriate communication technology depends on the unique characteristics of the project, such as the required range and data rate, and thus a variety of options are available for consideration [104]. A WSN-based application was presented by [105,106], who discussed some of the scheme’s advantages, Fuzzy-based Clustering Scheme, for example, attempts to reduce the deployment cost, propagation delay, and energy consumption while enhancing the reliability of the network. It is particularly well suited for large-scale monitoring applications of WSNs with higher node density. Will, real-world performance evaluation, addressing hardware and software requirements, comparing comparison with existing schemes, responding to network dynamics, and ensuring scalability in large-scale WSNs are all potential challenges should be discussed. Wi-Fi, Bluetooth, GPRS/3G/4G, ZigBee, LoRa, and SigFox were among the wireless technologies and protocol suites compared by the authors. Because of their suitable communication range and low power consumption, they demonstrated that LoRa and ZigBee wireless technologies are highly efficient for precision agriculture. It is mentioned that numerous strategies and algorithms linked to the energy efficiency of wireless sensor networks are classified. They also discussed the approaches that can be applied in PA. In the agricultural industry, 4G/3G wireless network technology is used to connect IoT-based smart devices for data sharing, precise assessment, accurate calculation, and so on. While the 3G/4G connectivity paradigm has shown great promise, there are some limitations that prevent the technology from reaching its full potential in the agricultural sector. One of the most significant limits is the operational area [107,108,109,110].

Despite the high speed and good connectivity provided by the 4G network, it is not feasible to interconnect all the smart farming devices in remote locations at a low installation and maintenance cost. To conduct demanding computational operations and run loaded services, a growing number of devices and a vast amount of research on IoT devices for smart farming are required. These devices also require increased intelligence, speed, scalability, secure communication capabilities, and processing power. Ultra-low latency and high connectivity are required for IoT devices to achieve quick performance and low costs. The utilization of 4G and 3G communication technologies has proven inadequate for real-time precision practices, primarily due to issues such as bandwidth limitations, connectivity issues, and slow data transfer speeds. In contrast, the integration of 5G technology within the agricultural sector has had a significant impact on various aspects, including real-time monitoring, unmanned aerial vehicles, virtual consultation, predictive maintenance, artificially intelligent robotics, data analytics, and cloud repositories. Consequently, the incorporation of 5G structures has facilitated improved speed, connectivity, scalability, and processing power, thereby enabling the resolution of existing limitations [100,112,113,114].