Environmental sensing and remote communication for smart farming: a review

Review Article
Md Sazzadul Kabir1Sumaiya Islam1Mohammod Ali2Milon Chowdhury1,2Sun-Ok Chung1,2Dong-Hee Noh3*


The need for precise, effective, and reliable measurement and monitoring of environmental parameters in greenhouses is critical for crop quality and yield. In the past few years, advanced senor methods garnered considerable study in the agriculture field. Capable and efficient use of intelligent sensors in a variety of activities is optimizing resource use while minimizing human interposition. Therefore, this review article aimed to provide significant knowledge about the detection and diagnosis of environmental parameters in greenhouses and the present state of remote communication utilizing intelligent approaches, as well as providing a broad overview of the field. A wide range of sensors and actuators are used extensively in advanced agricultural facilities like plant factories and greenhouses to monitor and regulate their environmental conditions. Temperature and humidity are the most important variables that affect plant growth. The ideal temperature range for healthy plant development is between 4°C and 30°C. Temperature and humidity sensors are widely used in greenhouses. CO2 concentration is critical for root growth and respiration. Photosynthesis and other physiological processes need an adequate amount of light and a photoperiod. CO2 sensors and light sensors are often used to monitor smart facilities. When it comes to nutrition monitoring, electrical conductivity (EC) and pH concentration are crucial factors to measure. The most frequent method of monitoring water quality and nutrient content is using pH sensors. Wireless communication such as ZigBee, LoRa, Bluetooth, WiFi, Sigfox, and GPRS/3G/4G technology is widely used for remote monitoring of the ambient environmental condition. The fast expansion of communication networks and the availability of a broad variety of new distant, proximal, and contact sensors are creating new options for farmers. The advancement of technology creates new opportunities for smart farming, and this review article will assist in the implementation of improved monitoring technologies in smart farming.



Information and communication technology plays a major role in managing the cyber and physical farm cycles under smart farming. Cloud computing and the internet of things are projected to accelerate this trend, allowing farmers to utilize artificial intelligence and more robots. Agricultural automation began with information technology and crop growth data collection. The data collection process necessitates the use of a sensor to collect environmental and growth data, as well as a server to store the data. The farmer then adjusts the environment and cultivates plants based on the collected data. Because the current smart farm is based on the greenhouse environment, the farmer's environment can be controlled by devices installed in the greenhouse such as fans, heaters, and air conditioners. Because wired communication systems have distance and location limitations, sensors cannot be installed in large areas of arcs, mountains, sea, or animals in the housing (Dhivya et al., 2012). Because the current technology level of the wireless communication system suffers from a power shortage problem, the development of the low power wireless communication module is being activated, and the change speed from the wired system to the wireless system is accelerating with the development of battery technology ( Uk-hyeon et al., 2016). The term big data generally refers to vast amounts of data of different types that can be gathered, analyzed, and used to make decisions. The collection and analysis of data from sensors on machines are facilitated by big data technologies, which play a key role in this development. Global agriculture uses more than 70% of available freshwater each year to irrigate only 17% of the land. On the other hand, due to fast increasing food demand and the effects of global warming, the total irrigated area is steadily shrinking (Pimentel et al., 2004; Taher et al., 2016). The world population will rise to eleven billion by the end of the century so many more mouths need to feed (Doknic, 2014) and (Grobkinsky et al., 2015). Growing urbanization and industry in both emerging and developed countries will lead to water shortages and challenges. Freshwater available for irrigated farming will decrease in the future (Playan et al., 2006; Levidow et al.,2014). Changing climate conditions, such as extreme weather, cyclone, hot waves, and rush would have a significant negative effect on food production. To produce enough food to satisfy food demand, agricultural systems must be able to grow more. In the absence of these steps, food insecurity would be a serious threat (Qui et al., 2018).

The main objective of environmental monitoring is to manage and minimize the impact an organization’s activities have on an environment, either to ensure compliance with laws and regulations or to mitigate risks of harmful effects on the natural environment and protect the health of human beings. Environmental data gathered using specialized observation tools, such as sensor networks and Geographic Information System (GIS) models, from multiple different environmental networks and institutes is integrated into air dispersion models, which combine emissions, meteorological, and topographic data to detect and predict concentration of air pollutants (Gang, 2006). Grab sampling and composite sampling (multiple samples) are used to monitor soil, set baselines, and detect threats such as acidification, biodiversity loss, compaction, contamination, erosion, organic material loss, salinization, and slope instability (Gray et al., 2017)

Technology, industry, and social patterns have all changed rapidly in the twenty-first century. Most industries have moved toward automation, and human intervention has decreased, resulting in an industrial revolution known as the fourth industrial revolution. Smart technologies are critical to long-term economic growth (Ashibani and Mahmoud, 2017). They convert houses, offices, factories, and even cities into self-contained, self-regulating systems that do not require human intervention. This modern automation trend, as well as the increasing use of cutting-edge technologies, is boosting the global economy. Both the Internet of Things (IoT) and Wireless Sensor Networks (WSN) are critical components of this modernization. The Internet of Things (IoT) is a branch of engineering primarily concerned with providing thousands of miniature, physically connected objects that can collaborate to achieve a common goal (Landaluce et al., 2020).

Under these conditions, it is critical to recognize that industrial and agricultural development are not mutually unique. On the road to resolving food security challenges, both sectors are complementary to one another. People started to explore the potential of implementing using various modern agricultural practices in the twentieth and twenty-first centuries. Precision farming is one of the most notable examples of agriculture. Precision farming practices are aimed at automating them to reduce crop losses due to climate fluctuations, soil-borne illness, insect infestations, and other factors. Several countries, including South Korea and Japan, are conducting various studies and research on the plant factory that can consistently offer valuable farm products regardless of location or climate. For plants to grow, plant factories regulate the environment (light, air, temperature, CO2, humidity, electrical conductivity, and mineral nutrition). A plant factory can produce agricultural goods all year long, regardless of weather or production levels, through controlled environmental conditions, and can boost the content of specific valuable qualities (Lee et al., 2018). Plant factories can obtain high added values through agriculture with such advantages. Environmental and soil protection concepts are being developed in many countries, but they are not applied. Eventually, the significant pollution of land, water, and plant long-term development becomes a top priority. A platform for plant factory monitoring management would minimize national emissions, regulate drinking water, and reduce human assets. Monitoring and controlling the plant environment through sensor technologies allow us to automatically manage and replace manpower in this facility. Furthermore, it is built on a wireless sensor network, ZigBee-based wireless communication, and sensor technology integration. Light, temperature, humidity, carbon dioxide concentration, and soil detection are among the wireless and wired sensing instruments (Akyildiz et al., 2018).

Environmental sensing and monitoring parameters in the smart greenhouse

Agricultural facilities such as large-scale greenhouses and plant factories rely on different types of sensors and actuators to control their environments. Monitoring and control are the major environmental parameters were discussed in the following sections.

Temperature and humidity monitoring

One of the most important environmental parameters that affect plant growth and development is temperature. A plant's growth is affected by temperature impacts both directly and indirectly. Plant roots absorb nutrients and water through their roots directly, while their leaves and air temperatures directly suffer from it. Plant growth and development are more directly affected by leaf temperature than by air temperature. Root development, respiration, transpiration, blooming, and dormancy may all be affected by temperature variations in an aeroponics growth chamber (Otazu et al., 2010). Therefore, the optimal temperature is critical for ensuring rapid plant quality and rising levels of plant material. Lighting source and airflow rate are two main environmental factors affecting the temperatures of leaves and air in a CEAL (Kitaya et al., 1998). Lighting sources emit thermal radiation, which increases the temperature difference between the leaf and air. It was reported that eggplant leaf temperatures were nearly 1°C higher under high-pressure sodium lamps, which produce more thermal radiation (Kitaya et al., 1998). Seedling growth in response to a change in air temperature between 18°C to 29°C. The dry weight of shoots was negatively affected by the high light intensity at 25°C; conversely, at 25°C and 300 µmol m-2s-1 light intensity, it was positively affected.

Growing sweet potato under fluorescent lights with 200 mol m-2s-1 and 16 hd−1 photoperiods produced significantly more propagules when the air temperature of the plants was increased (Fujiwara et al., 2004). Plants require different temperatures to develop, which can make their growth stage advance. In the long run, it will prove to be economically beneficial. The optimal growth chamber temperature for good plant growth should not be less than 4°C or greater than 30°C. Root development, respiration, transpiration, flowering, and dormancy may all be affected by temperature variations in an aeroponics growth chamber. Temperature sensors can be used in smart plant factories for monitoring temperature changes. Sensors gather temperature data from a single source and translate it into a format that can be understood by a device or observer. Humidity measurement is one of the most important issues in a variety of applications such as instrumentation, automated systems, agriculture, climatology, and geographic information systems (GIS). Utilization in intelligent systems and networks as monitoring sensors to determine the soil moisture during irrigation in agriculture, or for diagnosis of corrosion and erosion in infrastructures and civil engineering are among the applications of humidity sensors (Dean et al., 2012).

Therefore, humidity is an essential element in plant growth chamber conditions, and controlling it is recognized as critical for considerable plant development. The plant receives all of the available moisture in the growing chamber. A precise and accurate method for estimating moisture content in an indoor growth chamber would thus allow manufacturers to oversee their crops and provide them with an appropriate growing environment. A humidity sensor detects and measures the amount of water vapor within a room or enclosed space. Temperature sensors are now widely used in biomedical, food manufacturing, as well as environmental monitoring. However, relative humidity (Chen and Lu, 2005) is the most widely used unit for humidity measurement. Moisture sensor advancement has advanced significantly in recent years as a result of the use of a variety of sensing materials. Ceramics, polymers, and composites are three types of sensing materials employed in humidity sensors (Zor and Cankurtaran, 2016).

To maintain the wetness level, humidity sensors are typically installed in the plant environment. If the air humidity falls below the plant's requirements, the devices will relay information to the thermal decomposition nozzles to complete the operation. Table 1 represented some examples of different humidity and temperature monitoring sensors. The humidity detector is a sensor that measures how much moisture is in the soil at any particular time. Detectors integrated into the irrigation system make scheduling water demand and supply much easier. This assistance is used to achieve lower irrigation for optimal plant development (Doknic, 2014). Temperature sensors monitor the machinery that gathers the plants in addition to the plants that are gathered. When an equipment system requires minor maintenance, is underperforming, or is seriously malfunctioning, temperature sensors give out an alarm.

Table 1. Different types of smart farming inside humidity sensing sensors.http://dam.zipot.com:8080/sites/pastj/images/PASTJ_22-007_image/Table_PASTJ_22-007_T1.png

CO2 monitoring

The root environment must have a sufficient carbon dioxide concentration to maintain the core energy levels in the soil solution. Because low concentrations have an impact on root respiration and nutrient absorption, plant development is possible, and carbon dioxide level for the root environment is critical (Soffer and Burger, 1998). A carbon dioxide sensor is a device that measures the amount of CO2 in the atmosphere. According to (Bihlmayr,1988) CO2 detectors are used to assess the indoor climate in a housing complex so that supply airflow can be implemented. The CO2 sensor data measurement range, on the other hand, is 500 to 5,000 parts per million. CO2 sensors are mainly 2 types: nondispersive infrared carbon dioxide sensors and chemical carbon dioxide sensors. Nondispersive infrared carbon dioxide sensors detect carbon dioxide in the vapor phase by utilizing its recognizable intake and are made up of an active sensor, a noise filter, a lamp tube, and a thermal source. The CCDS of sensitive layers, on the other hand, is based on a low-energy pol-ymer or heteropoly siloxane. On the other hand Carbon dioxide measurement is required in ma-ny applications from building automation and greenhouses to life science and safety. Many technologies are used to measure CO2. Infrared sensing is the most widely applied one. The benefit of IR sensors are 1) stable and highly selective to the measured gas, 2) a long lifetime and 3) they withstand high humidity, dust, dirt, and other harsh conditions. CO2 monitoring sensors are shown in (Table 2) which is usually used in smart farming (Kalwinder., 2013)

Table 2. Different types of Smart farming inside CO2 sensing sensors.http://dam.zipot.com:8080/sites/pastj/images/PASTJ_22-007_image/Table_PASTJ_22-007_T2.png

Artificial light monitoring

Light is the primary source of energy for photosynthesis, as many other physiological functions improve plant growth and expansion. The plant reflects energy in the 400–700nm region, which is defined as photo-synthetically effective radio waves. Remote sensing observations of nighttime light provide us with a timely and spatially explicit measure of human activity, allowing us to track urbanization and socioeconomic dynamics, evaluate armed conflicts and disasters, investigate fisheries, assess greenhouse gas emissions and energy use, and analyze light pollution and health effects (Council et al., 2010). In a controlled environment, plant cultivation is done using high-pressure sodium lamps, metal halide lamps, fluorescent lamps, and light-emitting diodes (LED). The most common artificial light sources in modern agriculture are fluorescent, high-pressure sodium, metal halide, and a light-emitting diode. It is possible to control light and quality using light-emitting diodes, as they have a low cooling load, can withstand high temperatures, are unbreakable, have a solid frequency, and can be made to last a long time. According to (Shibuya et al.,1998) the intensity of light has an impact on many processes in the body related to plant growth and photochemical reactions that convert CO2 into carbohydrates, and it is considered a critical factor in regulating plant biosynthesis. The intensity of light that is efficient can increase the rate of photosynthesis and dry increased production. Light intensity has been shown in many studies to affect plant growth and light use quality, which is defined by the ratio of the stored light energy in the canopy of a tree to the chemical energy of its stem.. (Singh et al., 2015). The effect of maximum and minimum light intensities provided by 52 and 95 µmol m-2s-1 on seedling's growth rate and light use efficiency was compared. Growing plants in a plant factory with a light intensity of 300 µmol m-2s-1 greatly increased their fresh weight, leaf area, and chlorophyll content (in mg g-1 dry weight) (Tang et al., 2009). In terms of agricultural production, using an arbitrary light source device would save effort and money. Adjusting artificial light for current agricultural production necessitates collaboration among a wide range of researchers, including those from optical, bioengineering, and plant sciences. As a result, putting this modern agricultural technology into action is just around the corner. Artificial light monitoring sensors in (Table 3) are those usually used in smart farming.

Table 3. Different types Smart farming inside artificial light-sensing sensors.http://dam.zipot.com:8080/sites/pastj/images/PASTJ_22-007_image/Table_PASTJ_22-007_T3.png

Water and nutrients monitoring

Fertilizer application and pH and EC control of nutrient solutions are important to achieving success (Asao, 2012). The pH and EC levels are checked to avoid barrier growth. Because mineral water content in acidic, alkaline, and ion concentrations of all life forms in solutions differ, and concentration of the solution varies with solubility, measuring them is critical (Schwarz et al., 2013). EC and pH concentrations in liquid fertilizers that are not monitored will quickly create conditions in which crops cannot capture essential nutrients. If these concentrations are not adjusted, this will lead to harm and poor productivity of the plants (Asao, 2012).

The EC and pH density of the liquid fertilizer is a key property that must be monitored and controlled throughout crop production in these conditions. Furthermore, the EC and pH level of the liquid fertilizer is mostly measured manually via laboratory testing or modern equipment, which is a delayed process. A soil solution's EC level can rise or fall, for example, and the concentration of the mineral nutrients can be controlled by adding a high concentration nutrient solution or freshwater to the nutrient solution to keep the level within a prescribed range.

Maintaining the EC and pH values within the desired ranges, on the other hand, is a time-consuming and challenging task for farmers using traditional methods. Moreover, EC and pH sensors could be used to address the aforementioned issues. There are some water and nutrients monitoring sensors in (Table 4) that are usually used in smart farming.

Table 4. Different types of Smart farming inside CO2 sensing sensors.http://dam.zipot.com:8080/sites/pastj/images/PASTJ_22-007_image/Table_PASTJ_22-007_T4.png

Remote monitoring using wireless communication

For environmental monitoring and control, modern agricultural facilities such as plant factories and big greenhouses primarily use various types of communication systems. Different types of communication systems for monitoring and controlling smart farming were discussed in the following section.

IoT system

The characteristics of time atomization, air pressure, humidity levels, brightness, and carbon dioxide concentration make the network difficult to operate with a large amount of human labor and a greater need for expert knowledge and expertise. The grower, on the other hand, is in charge of keeping the aforementioned parameters within the proper range to provide the best possible growing conditions for the seedlings. Failure to precisely regulate and monitor the parameters may have a significant impact on the plant's growth and result in monetary loss. Because methods generally include certain automated means of supporting nutrient mist to plant roots regularly, refilling a nutrient reservoir, and managing light cycles and intensity, any component failure that happens while the driver is not on-site may be detected too late to prevent harm.

As a consequence, the cultivation was previously regarded as kind of inappropriate for the local farmer, and it is difficult to find a setup for a variety of reasons. (Kerns and Lee, 2017). The primary reason for the low social acceptance, however, is not an expense, but rather the level of care necessary of the grower with a wide range of expertise and good sense. For the reasons stated above, the employs more complex and advanced monitoring systems for early defect identification, real-time monitoring, and system control and automation. Subsequently, using artificial intelligence tools (Fig. 1) in smart farming to detect faults and diagnose issues on time would be useful. As an outcome, it may aid in minimizing rapid damage to plants growing and in fully automating.


Fig. 1.The automated greenhouse system using IoT technology (by Kerns and Lee, 2017).

Wireless system

This portion discusses various wireless specifications and standards that are used in farming. These network architectures are also compared to identify the most comfortable innovation in terms of energy consumption and affected quality, as both of these factors cause difficulties in an advanced agricultural application solution.

ZigBee-based wireless communication

The ZigBee wireless protocol is regarded as one of the most promising technologies for agricultural and farming applications. Because of its low duty cycle, ZigBee is well suited for PA applications requiring cyclic information updates, such as irrigated agriculture supervision, maintenance of water, seed treatment control (Cancela et al., 2015). Sensor networks in crop fields can connect with the access point or organizer node over long distances when the XBee series 2 is used (i.e.,100 m). For interior circumstances (e.g., greenhouses), ZigBee can minimize the transmission distance by up to 30 meters (Rani and Kamalesh, 2014).

Radio frequency identification and ZigBee are commonly used for sensor network applications. RFID is primarily used for identification purposes. ZigBee is well-suited to large-scale network deployments. The primary causes of this concept are its small size, reduced energy consumption, ease of use, and cheapness. A wireless sensor network is a device that can configure itself, network itself, diagnose itself, and heal itself. Many sensor nodes make up wireless sensor networks. One wireless personal area communications standard is IEEE 802.15.4. The media access control (MAC) and physical (PRY) layers are defined in this standard. This diagram depicts IEEE 802.15.4. Over the MAC layer, each factory can develop its network and application layers. Some agreements, such as the ZigBee alliance, establish system and application layer requirements (ZigBee alliance, April 2009).

Bluetooth (BT) - based wireless communication

The Bluetooth protocol has been used to establish connectivity between moveable and external devices, such as laptops, over a small distances of up to 10 meters. Because of its popularity and availability on most mobile devices, Bluetooth has been used to meet multi-level agricultural needs (Ojha and Raghuwanshi, 2015). The global positioning system and Bluetooth technologies are used to track weather, soil moisture, sprinkler location, and temperature from a far. The planned irrigation method was created to boost farm output while conserving water. The irrigation application presented uses the Bluetooth wireless connection protocol to collect field data in real-time ((Kim and Iversen, 2008). Bluetooth has also been used to create a variety of software and devices for monitoring relative humidity levels in greenhouses (Gang, 2006). Based on soil and meteorological data, a Bluetooth module was used in an embedded control method to operate the irrigation system in greenhouses, and this technology increased the number of leaves, size, dry mass, and fresh and dry weights of red and romaine lettuce in greenhouses.

When compared to the previous method, the integrated control approach utilizing Bluetooth technology increased the outcome of estimates for electricity and water by 90%. (That is, a timer control approach). Farmers can control water supply, monitor soil, and temperature variations, and use fertilizers and pesticides with better fuel economy, wide availability, and simplicity of use with smartphone-based Bluetooth (Andrew et al.,2013); (Zhihong et al.,2016); (Hana et al.,2016).

WiFi-based wireless communication

WiFi is the most renowned wireless network in compact devices like Tablets, mobile phones, laptop computers, and desktop computers. WiFi has available to support a range of approximately 20 m and 100 m in both inside and outside contexts. By joining various gadgets via the network, WiFi expands various designs in PA applications. The agricultural uses of mobile phones were estimated using Wi-Fi and 3G wireless technology (Chung et al., 2015). Protected crops have also been controlled and monitored via remote access and short messaging services. Surface temperatures, water content, weather moisture levels, sunshine intensity, and Carbon dioxide levels were saved in a gateway before being forwarded to the computer system via a WiFi network (Mohapatra et al., 2016). Three nodes make up the proposed system: a sensor, a router, and a server. The climatic conditions of the greenhouse or cropland, such as moisture, heat, air pressure, light, liquid level, and soil humidity, are all controlled. The study's goal is to reduce costs, cut cables, and improve the versatility of WSN sensing stations.

However, the suggested system's energy usage is extraordinarily high, at 42.17 J/h. Given that WiFi necessitates a lot of power, long transmission time, and a lot of data (Gray et al., 2018). Even though a WiFi server employs data redundancy techniques to reduce data loss, Connectivity is not suitable for farming applications. Furthermore, since WiFi cannot be used for multi-hop applications and is affected by the number of users and signal intensity, it is unsuitable for farm WSNs. Furthermore, because the WiFi nodes are constantly listening, power consumption will increase.

GPRS/3G/4G technology communication

The overall box radio service is a GSM-based cellular phone data packet service. Because GPRS has changeable delays due to the number of customers who use the same communication routes, resources, and developed a fully automated crop irrigation system information gathered by heat and moisture in the soil sensors installed at the root zone of plants using the GPRS subsystem and WSN, and considered this structure a cost-effective and workable solution for improving water quality in PA (Gutierrez et al., 2014). To evaluate a drip irrigation system, soil moisture was measured. An information management server and a WSN-GPRS point of entry were also employed in the development of a prototype system (Zhang et al., 2010). The WSN-GPRS gateway serves as a link between WSN and GPRS, allowing WSN data to be sent to a data management center (Navarro et al., 2015) used GPRS to measure and send soil, plant, and atmospheric data from multiple wireless nodes. Because of their self-sufficient character and utilization of solar energy, the wireless nodes have limitless autonomy. Using tablets, mobile phones, or PCs, different sensors may send data to a remote site through a GPRS network for additional analysis. All agricultural sensors are connected to the sensor board to collect farm data. The GPRS-board, which is highly reliant on a GSM/GPRS cellular connection, sends this data to the server for further analysis.

Long Range Radio (LoRa) protocol

The LoRa Alliance developed a radio packet switching for limited, wide-area internet permission to access used for indoor transmission. (2017) (Piti et al.) The fundamental network design of a LoRaWAN consists of LoRa end devices, LoRa gateways, and a LoRa server software. LoRa ends users connect with LoRaWAN gateways, which aid LoRa. End devices send raw LoRaWAN packets to a high-throughput LoRa network server via the fiber optic interface, which is typically 3G or Ethernet (Rani and Kamalesh, 2014). As a result, LoRa gateways act as a bidirectional communication or protocol adaptor with the LoRa network server. In this situation, the LoRa network server is in charge of decoding the data packets broadcast by LoRa devices and producing the frames that will be sent back to the devices. LoRa is a bidirectional solution that can be used in conjunction with machine-to-machine technologies such as Wifi or cellular. LoRa provides a low-cost method of connecting batteries or mobile devices to networks or end devices. The LoRa wireless protocol was used to track bee colonies in rural areas and ensure connectivity between the bee node and a remote server (Gil-Lebrero et al., 2016). LoRa has recently been used in several agriculture research projects, including improving kiwi production with a smart irrigation system (Italy), monitoring green areas with a smart garden system (Spain), improving corn yield fertilization methods (Italy), and improving banana yields (Colombia). (Laria et al., 2015).

SigFox protocol

SigFox is a reduced ultra-narrowband wireless mobile network that is ideal for the internet of things and machine-type communications networks. Phones, safety, mobile phones, the Internet, and television have all made use of SigFox (Laria et al., 2015). The SigFox network will be used to create a geolocation platform that will locate wildlife in mountain grasslands throughout the summer. A technique for assisting farmers in locating their livestock and increasing productivity was presented, emphasizing the importance of power consumption assessments, particularly when livestock are placed in high hilly areas (Terrasson et al., 2016).

Table 5 compares the wireless connectivity protocol mentioned above in terms of different parameters such as energy usage, connectivity range, price, network reliability, and others.

Table 5. Different wireless communication technologies.http://dam.zipot.com:8080/sites/pastj/images/PASTJ_22-007_image/Table_PASTJ_22-007_T5.png

Trends of environmental management in smart farm

Following the Green Revolution of the 1950s, agricultural production drove farming techniques, with little consideration given to the influence of sustainability. Traditional farming techniques, on the other hand, have progressed to the point where agricultural inputs are being misused, labor is in short supply, and energy demand is rising steadily. New opportunities in farming are emerging as a result of the rapid development of communication networks and the availability of a variety of new remote, proximal, and contact sensors ( (Gomiero and Paoletti, 2011). These technologies aid in the capture and transmission of location data real-time information at a low cost in the agricultural context (Mahan and Yeater, 2008). Once collected, processed, and analyzed, these data can aid in determining the state of modern agriculture (e.g., soil, crop, and climate) and, when combined with agro-climatic conditions and economic models, technical interventions can be implemented at the field level using either traditional or automated/robotized methods (Rose and Chilvers, 2018).

All of these factors are included in the notion of "smart farming," which refers to the use of current information and communication technologies in agriculture (Bacco et al., 2019). Variable-rate applicators (Nawar et al., 2017) and (Alameen et al., 2019). the Internet of Things (IoT) (Stamatiadis et al., 2020) and (Boursianis et al., 2020), geo-positioning systems (Muangprathub et al., 2019) and (Flaco et al., 2019), big data (Kamilaris et al., 2017) and (Bronson et al., 2016) unmanned aerial vehicles (UAVs, drones) (Tsouros et al., 2019), automated systems, and robotics are only a few examples. Smart farming is based on a precise and resource-efficient technique that aims to boost agricultural commodities production efficiency while also improving quality on a long-term basis (Hajjaj et al., 2016) and (Marinoudi et al., 2019). Smart farming, on the other hand, should provide added value to farmers in the form of more timely and accurate decision-making processes and/or more efficient extortion operations and management.

Future Application

Artificial intelligence farm techniques are regarded as a high-performing, ever-improving procedure for agricultural decision-making. Nowadays, it is rapidly gaining people's attention, becoming increasingly noticeable in our society, and changing constantly our social awareness and lifestyle. From the post through post-harvest, the methodologies provide for a variety of ways to measure plant growth and development. Smart farming is a novel agricultural plant cultivation method that is currently being developed. A review of the literature revealed that only a few studies on the use of smart farming technology in the intelligent farming system had been conducted. Furthermore, until recently, the majority of research has created a smart farming system employing a wireless sensor network employing ZigBee and Arduino systems with Bluetooth, a global mobile and WiFi system, and communication modules. During a review of the literature, we discovered that no one research had used cloud computing and big data approaches in smart farming to collect real-time data through the Internet. The method enables the operator to keep access to the system using portable devices such as mobile phones, touchscreen, and laptops from any location with an internet connection, regardless of weather, position, or period. The goal of image processing techniques is to measure and determine the physiology, progress, improvement, nutrient deficits, diseases, and other genetic aspects of plants using automated and non-destructive analysis.


This paper examines the current state of research and studies in the field of IoT and sensors used for remote sensing applications, as well as precision agriculture. The recommendations and discussion based on the review have been made in addition to the impact analysis of amount of research carried out in recent years in the area of study. Therefore framework has previously been viewed as somewhat unsuitable for the grower, and setup is uncommon for mentioned reasons above. Thus the difficult manual monitoring and auditing process may minimize the concept of the system's usefulness. The technology creates new opportunities for smart farming by increasing farmers' and producers' capability, dependability, and availability. This review paper will help with the implementation of improved monitoring technologies in smart farming. However, the method includes a wealth of data that greenhouse scientists may need to gain a better understanding of climate and fertilizers parameters which influence plant growth.


This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry (IPET) through the Smart Farm Innovation Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (Project No. 421035-04), Republic of Korea.


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