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Amertet, S.; Gebresenbet, G.; Alwan, H.M.; Vladmirovna, K.O. Assessment of Smart Mechatronics Applications in Agriculture. Encyclopedia. Available online: https://encyclopedia.pub/entry/46164 (accessed on 27 July 2024).
Amertet S, Gebresenbet G, Alwan HM, Vladmirovna KO. Assessment of Smart Mechatronics Applications in Agriculture. Encyclopedia. Available at: https://encyclopedia.pub/entry/46164. Accessed July 27, 2024.
Amertet, Sairoel, Girma Gebresenbet, Hassan M. Alwan, Kochneva Olga Vladmirovna. "Assessment of Smart Mechatronics Applications in Agriculture" Encyclopedia, https://encyclopedia.pub/entry/46164 (accessed July 27, 2024).
Amertet, S., Gebresenbet, G., Alwan, H.M., & Vladmirovna, K.O. (2023, June 28). Assessment of Smart Mechatronics Applications in Agriculture. In Encyclopedia. https://encyclopedia.pub/entry/46164
Amertet, Sairoel, et al. "Assessment of Smart Mechatronics Applications in Agriculture." Encyclopedia. Web. 28 June, 2023.
Assessment of Smart Mechatronics Applications in Agriculture
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This entry is adapted from the peer-reviewed paper 10.3390/app13127315

The agriculture sector industry is encountering growing global demand for food but also demand for transparency in food supply chains from food consumers. Because of this, modern, complex methods are necessary, and the significantly increased use of modern mechatronics systems can be recommended. Precision agriculture, a development in mechatronics, is already playing a significant role in agricultural industries, where it has minimized labor requirements and decreased crop production costs by maximizing output. The main benefit of mechatronics system integration in agriculture, however, is a doubling in efficiency compared with manually controlled machines, and this has enabled a revolution in how agricultural crops are established, managed, and harvested 

mechatronics robotics system automation robotics agriculture mechanism

1. Introduction

According to the Food and Agriculture Organization (FAO), 821 million people worldwide are underfed owing to current imbalances in the global food supply (REF). In particular, the 2023 Global Hunger Index (GHI) report showed that many African countries, including Ethiopia, Kenya, Sudan, Somalia, Nigeria, and Mali, are experiencing severe hunger (REF). One of the main causes of low agricultural productivity in most developing countries is the lack of suitable agricultural machinery for field and processing operations. Machines and semi-mechanical systems are used in most European countries to maximize production, resulting in yields that have fed the rest of the world until now. Future agriculture in developing countries will require more advanced, fully automated mechatronics systems in order to expand the range of production from subsistence to commercial production, processing, packaging, storage, and delivery. This will reduce the human drudgery involved in farm work, allow more work to be done in less time, improve the efficiency and timeliness of field operations, and increase efficiency in post-harvest operations. By helping to meet the demand for agricultural output, smart mechatronics systems would also be very important for society as a whole [1][2][3][4][5][6][7].
In recent decades, industrial production and goods handling have been more efficient and less expensive, thanks in large part to automation and robotics. Similar changes have occurred in the agriculture sector over the past few years, with, e.g., self-guiding tractors and harvesters. In addition, GPS and vision-based systems are already being sold commercially. More recently, researchers have begun to experiment with autonomous systems that integrate field tasks, such as planting, spraying, mowing, and weeding, with other essential tasks, such as thinning, trimming, and harvesting.
Because of their efficiency-related benefits and other advantages, smart mechatronics systems are becoming crucial in the agriculture sector, e.g., in aquaculture production, food processing, building environment management, irrigation systems, tractor and industrial systems, and grain drying. However, some key questions remain unanswered, including: Can production rates be increased further through the incorporation of smart mechatronics systems? Why have existing systems not addressed food security issues so far? A glance at the literature suggests that many earlier studies were more concerned with the modules than with the control systems in which these modules would function best, while mechatronics applications and impacts on agriculture were not taken into account. Some research papers have recommended modules for use in mechatronics that are not suitable for high performance and can lead to excessive setting, a short attention span, excessive multi-tasking, a risk of privacy invasion, a risk of limited learning, dependence, time wasted, and other diversions.

2. Assessment of Smart Mechatronics Applications in Agriculture

Agriculture is one of the oldest industries, dating as far back as the nomadic age originally, when it depended solely on human effort. Draft animals were brought into use later, and then came mechanical advances, such as diesel/steam-engine tractors and mechanical tools with hydrostatic power. This modernization process is often mistakenly thought to benefit only industrialized countries with highly mechanized agriculture, but there has also been some mechanization of agriculture in developing countries, such as Ethiopia, Egypt, and Afghanistan, which do not have smart mechatronics systems. However, in most current agriculture, mechanization systems are not fully automated. Lack of automation, e.g., mechatronics systems, is a concern that needs to be addressed, particularly in developing countries, to increase the production capacity of their numerous small- and medium-sized farms, and even large-scale farms. 

Scientific and technological developments have considerably advanced agricultural production, particularly in “smart agriculture” (precision agriculture). The use of mechatronics (automation and artificial intelligence) in agriculture is growing. In addition, modern mechatronics practices and goods now differ greatly from those of a few decades ago. In particular, modern products and processes being developed adopt a multidisciplinary perspective and target integration, sophistication, robustness, intelligence, and feedback. As a result, the term “mechatronics” was created from the words “mechanism”, “computer”, “control theory”, and “electronics” [8][9][10][11][12][13].
The main goal of smart mechatronics systems is to optimize the application of farm inputs under changing field conditions. They can be used to observe and record various information via communication technology (satellite, GPS, GIS, sensors, electronic systems, computer, camera), identify differences in crops or animals, and apply decision-making information to manage the agricultural components (soil, water, farm inputs, microclimate, environment, machinery, and machinery-related parameters) to achieve optimal and sustainable crop and livestock production. Precision agriculture is essentially about monitoring, measuring, and responding to intra- and intra-farm variation. It involves the management of a field despite adverse conditions with the aim of increasing production, and thereby the profitability of crop or livestock production, without causing soil degradation. The aim of smart agriculture is not to achieve the same production everywhere but rather to direct the precise input needed to achieve site-specific returns that increase long-term revenue for that site with minimal input. Precision agriculture can be seen as an observation, impact assessment, and timely strategic response to subtle variations in the causal components of agricultural production. It can be used on different types of farms and can be applied to the pre- and post-production aspects of a farm. Consequently, precision agriculture is divided into eight categories based on its specific applications [14][15][16][17]. These are:
✓
Guidance systems: This allows the exact direction of operations within the field and helps to avoid overlapping application zones.
✓
Precise seeding: This gives a consistent number of seeds sown, accurate alignment of seeds (with constant spacing), and minimum variation in seeding density.
✓
Fertilizer application: The volume of fertilizer applied can be adjusted to the real nutritional status inside the field.
✓
Plant protection: The amount of pesticides used (herbicide, fungicide, and insecticide) within a field can be varied according to crop requirements.
✓
Soil management: Tillage (ploughing intensity/depth) can be optimized based on soil properties.
✓
Irrigation: Precise irrigation according to soil water status.
✓
Yield mapping: For quality control, management decisions, and yield maximization.
✓
Documentation: All actions can be documented precisely for each management zone, including information about the total amount of materials and working hours.
The smart mechatronics systems currently used in farming and agricultural processing to increase efficiency, productivity, and sustainability in food production are of several different types, such as precision agriculture, smart irrigation, biotechnology, and automation. Furthermore, there have been significant technological advances in areas such as indoor vertical farming, livestock technology, modern greenhouse production, artificial intelligence, and block chain.
Figure 1 depicts the total software architecture and its joint hardware design. It is made up of three primary parts. The creation of a graphical user interface (GUI) is the initial step in controlling and managing the farming robot. The user and the agricultural robot are connected via a cloud service rather than directly. The Network Platform for Internet of Everything (NETPIE) offers several functionalities. As a result, NETPIE is used to transfer instructions from the user to the agricultural robot. There are two basic parts of the farming robot. Raspberry Pi, which serves as a server, is the first. To communicate, clients can establish a connection to Raspberry Pi’s IP address. The farming robot’s second component, the Arduino, which controls motor movements, receives commands from Raspberry Pi. The simultaneous control of all four linear bipolar motor drivers is made possible by the Arduino board. Watering and seeding tools can be carried by the universal tool mount, which can be moved to any desired location. Additionally, Arduino is in charge of operating a vacuum pump for the seeder and solenoid valves for the watering tool. The application first establishes a link between Raspberry Pi and Arduino. Through serial communication, these two boards talk to one another. Arduino’s port is defined once the Python serial library is added, which creates the connection. The Raspberry Pi then has an IPv4 address and functions as a server. The server and port 8000 are given IP addresses by the application. The server has the capability to connect to a number of clients at once. The client is prepared to communicate with Raspberry Pi across the local network once the connection has been made. The server is made to accept commands from clients iteratively by utilizing a loop, as otherwise, the server can only accept one command and approach. The inputs are encoded using “UTF-8” Unicode character encoding [18][19][20][21][22][23].
/media/item_content/202306/649bfb2e02522applsci-13-07315-g002.png
Figure 1. Hardware and software of smart agriculture systems [22].
Figure 2 illustrates future farming technologies. The technology is made up of three components: a front node (an IoT ECO box), edge computing (an IoT gateway), and cloud computing (big data analysis). ZigBee, LoRA, and WiFi are used to connect the front node to edge computing, while 5G/4G LTE and an RJ-45 1G WireLAN connector are used to connect edge computing to cloud computing in order to efficiently complete tasks. Components of the front node include various controls, sensors, devices, and environmental elements, such as water, plants, soil, moisture, temperature, light, and cameras. Smart planning, controller, monitoring, and communications are all linked with edge computing. Virtual store appliance H261-H61 and database analysis are two components of cloud computing. Overall, smart agriculture technology controls the environment right away to provide plants with the best possible conditions for growth. If the scenario allows, remote monitoring through mobile application, using the remote control for manual processing, it may gather environmental data and perform big data analysis using the cloud database server to offer better agricultural growing circumstances [23][24][25][26][27].
/media/item_content/202306/649bfb492b484applsci-13-07315-g003.png
Figure 2. Automization and robotization agriculture system [22].
A number of these emerging agricultural technologies are described in sections A–S below:
(A)
 Autonomous farm machinery
Modern autonomous machines and equipment that can be used in agriculture with little or no human intervention have been developed and commercialized. They are based on robotic technology and can process real-time farm data and then carry out the corresponding agricultural process, which includes cultivation, planting, seeding, weeding, fertilizing, and spraying, among other tasks. Revolutionary technological developments, from autonomous agricultural machines to the use of digital agriculture, include: GPS-enabled tractors, which can be used in modern agriculture to achieve controlled cultivation that provides a uniform land area for uniform planting and/or seeding, and uniformly applied fertilizer and crop sprays. In addition, these tractors have an advanced mechanism that allows independent control of engine and machine speed and a GPS-based remote-controlled robot that integrates built-in autonomous navigation software [28][29][30][31].
(B)
 Drone-supported farming
In drone-supported farming, aerial photography can be carried out with IoT-compatible aerial drones to create agricultural vegetation indices, field mapping, and remote farm monitoring. Drones can also integrate IoT sensors to provide highly accurate and real-time farm data on parameters such as weather, crop height, water saturation, pest and weed detection, etc., which are important for crop growth stages, zoning, and crop classification, monitoring, seeding, and spraying [32][33][34][35][36].
(C)
 Smart dairy farming
A smart dairy farm with automated milking, feed mixing, feed wagons, manure handler, and animal monitoring can be achieved with the following mechatronics systems: an automatic milking system (AMS), which creates a faster and more convenient milking regime, combined with real-time quality and quantity data collection. The milk analysis parameters displayed on the screen can be very important in monitoring the daily nutrition of the cows and also provide an assessment of the general health status of the cows and their milking pattern [37][38][39][40][41].
(D) 
IoT-enabled livestock monitoring
This is achieved by fitting cattle with ear tag chip sensors that collect data on, e.g., body blood pressure, pulse, temperature, and rumination activity. Animal health can then be analyzed by algorithms to identify potential individual herd infections and recommend potential treatment options. In this way, the farmer can improve the health status of the herd. One such system (Zoetis, Troy Hills, NJ, USA) implemented chip placement using Smartbow technology [42][43][44][45][46].
(E)
 Smart poultry farming
This includes automatic egg collection, automatic distribution of food and water, and an automatic monitoring system that precisely maintains the desired environmental conditions on the poultry farm. The main technological applications on smart poultry farms include (1) IoT sensors that monitor real-time environmental conditions, including ammonia gas, humidity, light, temperature, etc.; (2) an integrated GPRS module that provides convenient remote monitoring; and (3) GSM modules so that the grower can monitor developments in a timely manner and receive intruder warnings if possible [47][48][49].
(F) 
Smart greenhouses
The latest greenhouse technology can be integrated with new IoT-based solar energy smart greenhouse systems. Automation technologies that a smart greenhouse system integrates to achieve sustainable agriculture include (1) use of IoT sensors to collect greenhouse data on environmental parameters, such as humidity, temperature, light, soil moisture, concentration, and pH; (2) a photovoltaic-thermal (PVT) solar energy-based system to generate photovoltaic energy, which is necessary for operation of the electrical system and thus economical; and (3) Wireless Sensor Network (WSN) nodes that provide cloud storage and thus enable remote control of the greenhouse system [50][51][52][53].
(G)
 Smart irrigation
Mechatronics and automation technologies can be used to develop a modern smart irrigation system that operates on real-time field data by combining and deploying the following technologies: (1) IoT-based sensor modules distributed at strategic locations (i.e., nodes) on the farm to monitor various parameters including temperature, humidity, soil moisture, and water level; and (2) CoT-based thermal imaging, which enables remote field surface temperature mapping and water content analysis in different regions, and therefore offers a technique that favors less irrigated areas to ensure equal distribution of water in the field [54][55][56].
(H) 
Smart warehousing
Smart warehousing can help in the effective monitoring and control of farm produce. With the help of IoT sensors, automatic, and timely re-ordering of farm supplies and machinery spare parts can be done, which ensures continuous operation of the farm, reducing farm breakdown minimal waste of time, and lower inventory costs [57]. In addition, RFID (Radio Frequency Identification) sensors can be used to clearly mark the farm’s produce, enabling safe and accurate tracking throughout the supply chain, i.e., from field to wholesale and then to retailers who distribute it to consumers. An IoT-based storage system can automatically monitor farm crop conditions to create optimal conditions to reduce post-harvest losses, improve yield, and increase farm productivity. Therefore, this intelligent warehouse technology implementation on the farm can ensure (1) agricultural evaluation metrics, where points and indices can be given to the farmer and consumers based on value-based activities; (2) goal setting from feedback based on farm processes and/or products; and (3) RFID-based blockchain sustainability, providing food tracking from farm harvest and storage to delivery and distribution to consumers [58][59][60].
(I)
 Auto-steering
Tractors that combine GIS-based terrain mapping can be used for a range of field operations, from cultivation to harvesting. These autonomous tractors [61] have a 3D laser scanner, GPS-enabled cameras, and other multiple sensors that detect various parameters, such as terrain and weather conditions [62].
(J)
 Smart harvesting machinery
An integrated camera surveillance system in smart harvesting machines can be used to provide the operator with a wider field of view while working in the field. This improved machine control range improves machine performance in the field. In addition, a robotic harvester with advanced GPS integration and improved accuracy has been developed, which may be another good candidate for automating farm harvesting operations [63][64].
(K) 
Precision farming
With the introduction of digital agriculture, real-time and accurate information can be collected from the field, leading to the development of data-based agriculture. This information can be used to determine soil and crop properties, improve productivity, monitor progress, predict yields, and use natural resources optimally to achieve environmental sustainability. Implementing precision farming can also help reduce resource waste and increase farm profit margins [65][66].
(L)
 Farming productivity
Modern automated farming methods contribute greatly to the mechanization of agriculture and fulfil the operational needs of the farm. Although technological innovations are very reliable, production stops when agricultural machinery or agricultural systems fail. However, these failures or malfunctions occur periodically, and third-party service providers may provide remote troubleshooting, maintenance, and repair. The farmer may also need to keep a large inventory of machine spare parts to minimize machine downtime and ensure that work continues even after a breakdown. The implementation of agricultural technical systems increases farm yields and thus productivity [67][68].
(M) 
Training requirements
It is possible that farmers may find it difficult to adapt to digital farming technology and interpret computerized results and may also experience operational difficulties due to various integrated technical systems. This may require the farmer to invest in practical training and introduction to the use of agricultural machinery, and even learn the basic concepts of calculation to effectively operate, implement, and operate agricultural systems. Sometimes, these steps can be time consuming, difficult, stressful, or even inadequate [69][70].
(N) 
Employment opportunities
A downside of the introduction of new farming techniques is that it will make agricultural workers unemployed. The fact that these farming systems are almost completely autonomous means that less human labor is required. Therefore, a tradeoff arises between the level of implementation of mechanized agricultural systems and the loss of livelihoods [71].
(O) 
Land use
The use of highly mechanized, faster, and large-scale farm automation technology and machinery can result in more land being used for useful and productive agriculture and reduce the need for human labor. This means that farm yields increase, which in turn ensures better returns for farmers and food sustainability for the economy [72].
(P)
  Mobile applications
With the latest smartphone technology, farmers can now more easily and conveniently integrate farm automation technologies with remote monitoring from their smartphones and tablets [73].
(Q) 
Blockchain technology
This allows for accurate tracking along the supply chain of all products within the food system. With block chain technology, food contamination can be traced back to the exact source [74].
(R)
 Mini chromosome technology
This new technology retains the plant’s original chromosomes, making it a more socially acceptable means of crop enhancement than other methods of genetic modification [75].
(S)
 Mechatronics system applications in different countries
Most developed countries have spent much time and effort developing smart mechatronics systems. These include: Clearpath Robotics in Canada, Earth Dynamics, Modular Robotics, and Soteria Mechatronics in the USA, Reshape Biotech in Denmark, VERHAERT in Belgium, Stanley Robotics and ESTEC in France, Cambridge Mechatronics in the UK, Advantest in Japan, Sartorius in Germany, TREVENTUS Mechatronics in Austria, and BFG Group in Russia [76].

References

  1. Banu, S. Precision agriculture: Tomorrow’s technology for today’s farmer. J. Food Process. Technol. 2015, 6, 8.
  2. Khan, N.; Ray, R.L.; Kassem, H.S.; Zhang, S. Mobile Internet Technology Adoption for Sustainable Agriculture: Evidence from Wheat Farmers. Appl. Sci. 2022, 12, 4902.
  3. Castellanos, G.; Deruyck, M.; Martens, L.; Joseph, W. System assessment of WUSN using NB-IoT UAV-aided networks in potato crops. IEEE Access 2020, 8, 56823–56836.
  4. Li, M.; Shamshiri, R.R.; Schirrmann, M.; Weltzien, C. Impact of camera viewing angle for estimating leaf parameters of wheat plants from 3D point clouds. Agriculture 2021, 11, 563.
  5. Xydis, G.A.; Liaros, S.; Avgoustaki, D.-D. Small scale Plant Factories with Artificial Lighting and wind energy microgeneration: A multiple revenue stream approach. J. Clean. Prod. 2020, 255, 120227.
  6. Antle, J.M.; Capalbo, S.M.; Mooney, S.; Elliott, E.T.; Paustian, K.H. Economic analysis of agricultural soil carbon sequestration: An integrated assessment approach. J. Agric. Resour. Econ. 2001, 26, 344–367.
  7. Hasan, M.; Tanawala, B.; Patel, K.J. Deep learning precision farming: Tomato leaf disease detection by transfer learning. In Proceedings of the 2nd International Conference on Advanced Computing and Software Engineering (ICACSE), Sultanpur, India, 8–9 February 2019.
  8. Zheng, H.; Ma, W.; Wang, F.; Li, G. Does internet use improve technical efficiency of banana production in China? Evidence from a selectivity-corrected analysis. Food Policy 2021, 102, 102044.
  9. Mondal, A.; Chakrabarti, A.B. Information and Communication Technology Adoption Strategies of Emerging Multinationals From India. J. Glob. Inf. Manag. 2021, 29, 161–175.
  10. Twumasi, M.A.; Jiang, Y.; Zhou, X.; Addai, B.; Darfor, K.N.; Akaba, S.; Fosu, P. Increasing Ghanaian fish farms’ productivity: Does the use of the internet matter? Mar. Policy 2021, 125, 104385.
  11. Zhu, X.; Hu, R.; Zhang, C.; Shi, G. Does Internet use improve technical efficiency? Evidence from apple production in China. Technol. Forecast. Soc. Chang. 2021, 166, 120662.
  12. Morrar, R.; Abdeljawad, I.; Jabr, S.; Kisa, A.; Younis, M.Z. The role of information and communications technology (ICT) in enhancing service sector productivity in Palestine: An international perspective. J. Glob. Inf. Manag. 2019, 27, 47–65.
  13. Magwaza, S.T.; Magwaza, L.S.; Odindo, A.O.; Mditshwa, A. Hydroponic technology as decentralised system for domestic wastewater treatment and vegetable production in urban agriculture: A review. Sci. Total Environ. 2020, 698, 134154.
  14. Krishnan, R.S.; Julie, E.G.; Robinson, Y.H.; Raja, S.; Kumar, R.; Thong, P.H. Fuzzy logic based smart irrigation system using internet of things. J. Clean. Prod. 2020, 252, 119902.
  15. Khan, F.A. A review on hydroponic greenhouse cultivation for sustainable agriculture. Int. J. Agric. Environ. Food Sci. 2018, 2, 59–66.
  16. Pawlowski, A.; Guzman, J.L.; Rodríguez, F.; Berenguel, M.; Sánchez, J.; Dormido, S. Simulation of greenhouse climate monitoring and control with wireless sensor network and event-based control. Sensors 2009, 9, 232–252.
  17. Sott, M.K.; Nascimento, L.d.S.; Foguesatto, C.R.; Furstenau, L.B.; Faccin, K.; Zawislak, P.A.; Mellado, B.; Kong, J.D.; Bragazzi, N.L. A bibliometric network analysis of recent publications on digital agriculture to depict strategic themes and evolution structure. Sensors 2021, 21, 7889.
  18. Karpagam, J.; Merlin, I.I.; Bavithra, P.; Kousalya, J. Smart irrigation system using IoT. In Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 6–7 March 2020; pp. 1292–1295.
  19. Fareed, A.; Ullah, M.G.; Hussain, A.; Zaidi, M.; Baig, M. Smart irrigation care unit. In Proceedings of the 2017 First International Conference on Latest Trends in Electrical Engineering and Computing Technologies (INTELLECT), Karachi, Pakistan, 15–16 November 2017; pp. 1–5.
  20. Murugan, M.B.; Rajagopal, M.K.; Roy, D. IoT Based Smart Agriculture and Plant Disease Prediction. J. Phys. Conf. Ser. 2021, 2115, 012017.
  21. Arogundade, O.; Qudus, R.; Abayomi-Alli, A.; Misra, S.; Agbaegbu, J.; Akinwale, A. A Mobile-Based Farm Machinery Hiring System. In Proceedings of the Second International Conference on Computing, Communications, and Cyber-Security, Delhi, India, 24–27 September 2021; pp. 213–226.
  22. Karmokar, C.; Hasan, J.; Khan, S.A.; Alam, M.; Arduino, I.I. UNO based smart irrigation system using GSM module, soil moisture sensor, sun tracking system and inverter. In Proceedings of the 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET), Chittagong, Bangladesh, 27–28 October 2018; pp. 98–101.
  23. Bolu, C.A.; Azeta, J.; Alele, F.; Daranijo, E.O.; Onyeubani, P.; Abioye, A.A. Solar powered microcontroller-based automated irrigation system with moisture sensors. J. Phys. Conf. Ser. 2019, 1378, 032003.
  24. Khanna, A. Agro-based sensor’s deployment for environmental anticipation: An experimental effort for minimal usage of water within agricultural practices. Culture 2020, 4, 219–236.
  25. Bechar, A.; Vigneault, C. Agricultural robots for field operations: Concepts and components. Biosyst. Eng. 2016, 149, 94–111.
  26. Bechar, A.; Vigneault, C. Agricultural robots for field operations. Part 2: Operations and systems. Biosyst. Eng. 2017, 153, 110–128.
  27. Thomasson, J.A.; Baillie, C.P.; Antille, D.L.; Lobsey, C.R.; McCarthy, C.L. Autonomous Technologies in Agricultural Equipment: A Review of the State of the Art; American Society of Agricultural and Biological Engineers: St. Joseph, MI, USA, 2019.
  28. dos Santos, A.F.; da Silva, R.P.; Zerbato, C.; de Menezes, P.C.; Kazama, E.H.; Paixão, C.S.; Voltarelli, M.A. Use of real-time extend GNSS for planting and inverting peanuts. Precis. Agric. 2019, 20, 840–856.
  29. Bochtis, D.; Sorensen, C.G.; Kateris, D. Operations Management in Agriculture; Academic Press: Cambridge, MA, USA, 2018.
  30. Khan, N.; Ray, R.L.; Kassem, H.S.; Hussain, S.; Zhang, S.; Khayyam, M.; Ihtisham, M.; Asongu, S.A. Potential Role of Technology Innovation in Transformation of Sustainable Food Systems: A Review. Agriculture 2021, 11, 984.
  31. Zhang, X.; Cao, Z.; Dong, W. Overview of edge computing in the agricultural internet of things: Key technologies, applications, challenges. IEEE Access 2020, 8, 141748–141761.
  32. Khan, N.; Ray, R.L.; Sargani, G.R.; Ihtisham, M.; Khayyam, M.; Ismail, S. Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture. Sustainability 2021, 13, 4883.
  33. Nataraj, E.; Sarkar, P.; Raheman, H.; Upadhyay, G. Embedded digital display and warning system of velocity ratio and wheel slip for tractor operated active tillage implements. J. Terramechanics 2021, 97, 35–43.
  34. Kim, W.S.; Lee, W.S.; Kim, Y.J. A review of the applications of the internet of things (IoT) for agricultural automation. J. Biosyst. Eng. 2020, 45, 385–400.
  35. Pérez-Escamilla, R. Food security and the 2015–2030 sustainable development goals: From human to planetary health: Perspectives and opinions. Curr. Dev. Nutr. 2017, 1, e000513.
  36. Reza Anik, A.; Rahman, S.; Sarker, J.R. Five Decades of Productivity and Efficiency Changes in World Agriculture (1969–2013). Agriculture 2020, 10, 200.
  37. Kumar, S.; Tiwari, P.; Zymbler, M. Internet of Things is a revolutionary approach for future technology enhancement: A review. J. Big Data 2019, 6, 111.
  38. Kumar, N.; Upadhyay, G.; Choudhary, S.; Patel, B.; Naresh Chhokar, R.S.; Gill, S.C. Resource conserving mechanization technologies for dryland agriculture. In Enhancing Resilience of Dryland Agriculture under Changing Climate: Interdisciplinary and Convergence Approaches; Springer Nature: Singapore, 2023; pp. 657–688.
  39. Duarte-Galvan, C.; Torres-Pacheco, I.; Guevara-Gonzalez, R.G.; Romero-Troncoso, R.J.; Contreras-Medina, L.M.; Rios-Alcaraz, M.A.; Millan-Almaraz, J.R. Advantages and disadvantages of control theories applied in greenhouse climate control systems. Span. J. Agric. Res. 2012, 10, 926–938.
  40. Li, J.; Li, L.; Wang, H.; Ferentinos, K.P.; Li, M.; Sigrimis, N. Proactive energy management of solar greenhouses with risk assessment to enhance smart specialisation in China. Biosyst. Eng. 2017, 158, 10–22.
  41. Tong, X.; Sun, Z.; Sigrimis, N.; Li, T. Energy sustainability performance of a sliding cover solar greenhouse: Solar energy capture aspects. Biosyst. Eng. 2018, 176, 88–102.
  42. Ramani, U. User Friendly with Zigbee Technology Control Agricultural Automation using Lab view. Ann. Romanian Soc. Cell Biol. 2021, 25, 7854–7861.
  43. Bersani, C.; Ruggiero, C.; Sacile, R.; Soussi, A.; Zero, E. Internet of Things Approaches for Monitoring and Control of Smart Greenhouses in Industry 4.0. Energies 2022, 15, 3834.
  44. Aafreen, R.; Neyaz, S.Y.; Shamim, R.; Beg, M.S. An IoT based system for telemetry and control of Greenhouse environment. In Proceedings of the 2019 International Conference on Electrical, Electronics and Computer Engineering (UPCON), Aligarh, India, 8–10 November 2019; pp. 1–6.
  45. Trendov, M.; Varas, S.; Zeng, M. Digital Technologies in Agriculture and Rural Areas: Status Report; FAO: Rome, Italy, 2019.
  46. Ruan, J.; Shi, Y. Monitoring and assessing fruit freshness in IOT-based e-commerce delivery using scenario analysis and interval number approaches. Inf. Sci. 2016, 373, 557–570.
  47. Shi, L.; Shi, G.; Qiu, H. General review of intelligent agriculture development in China. China Agric. Econ. Rev. 2018, 11, 39–51.
  48. Khan, N.; Siddiqui, B.N.; Khan, N.; Khan, F.; Ullah, N.; Ihtisham, M.; Ullah, R.; Ismail, S.; Muhammad, S. Analyzing mobile phone usage in agricultural modernization and rural development. Int. J. Agric. Ext. 2020, 8, 139–147.
  49. Huq, M.E.; Fahad, S.; Shao, Z.; Sarven, M.S.; Al-Huqail, A.A.; Siddiqui, M.H.; ur Rahman, M.H.; Khan, I.A.; Alam, M.; Saeed, M.; et al. High arsenic contamination and presence of other trace metals in drinking water of Kushtia district, Bangladesh. J. Environ. Manag. 2019, 242, 199–209.
  50. Sivarethinamohan, R.; Yuvaraj, D.; Priya, S.S.; Sujatha, S. Captivating profitable applications of artificial intelligence in agriculture management. In International Conference on Intelligent Computing & Optimization; Springer: Cham, Switzerland, 2021; pp. 848–861.
  51. Mittal, S.; Mehar, M. Socio-economic factors affecting adoption of modern information and communication technology by farmers in India: Analysis using multivariate probit model. J. Agric. Edu. Ext. 2016, 22, 199–212.
  52. Helfer, G.A.; Barbosa, J.L.; Alves, D.; da Costa, A.B.; Beko, M.; Leithardt, V.R. Multispectral cameras and machine learning integrated into portable devices as clay prediction technology. J. Sens. Actuator Netw. 2021, 10, 40.
  53. Islam, A.; Shin, S.Y. Bus: A blockchain-enabled data acquisition scheme with the assistance of uav swarm in internet of things. IEEE Access 2019, 7, 103231–103249.
  54. Debauche, O.; Trani, J.-P.; Mahmoudi, S.; Manneback, P.; Bindelle, J.; Mahmoudi, S.A.; Guttadauria, A.; Lebeau, F. Data management and internet of things: A methodological review in smart farming. Internet Things 2021, 14, 100378.
  55. Debauche, O.; Mahmoudi, S.; Manneback, P.; Assila, A. Fog IoT for Health: A new Architecture for Patients and Elderly Monitoring. Procedia Comput. Sci. 2019, 160, 289–297.
  56. Debauche, O.; Mahmoudi, S.; Mahmoudi, S.A.; Manneback, P.; Lebeau, F. A new edge architecture for ai-iot services deployment. Procedia Comput. Sci. 2020, 175, 10–19.
  57. Terenzi, A.; Cecchi, S.; Spinsante, S. On the importance of the sound emitted by honey bee hives. Vet. Sci. 2020, 7, 168.
  58. Cecchi, S.; Spinsante, S.; Terenzi, A.; Orcioni, S. A smart sensor-based measurement system for advanced bee hive monitoring. Sensors 2020, 20, 2726.
  59. Debauche, O.; Mahmoudi, S.; Mahmoudi, S.A.; Manneback, P.; Bindelle, J.; Lebeau, F. Edge computing and artificial intelligence for real-time poultry monitoring. Procedia Comput. Sci. 2020, 175, 534–541.
  60. Elmoulat, M.; Debauche, O.; Mahmoudi, S.; Brahim, L.A.; Manneback, P.; Lebeau, F. Monitoring system using internet of things for potential landslides. Procedia Comput. Sci. 2018, 134, 26–34.
  61. Razali, M.H.; Roslan, S.; Abd Halim, A.S.M.; Basit, H. Design and Development of Mechatronic Application in Agricultural Irrigation Device. World J. Eng. Technol. 2016, 4, 450.
  62. Tashakkori, R.; Hamza, A.S.; Crawford, M.B. Beemon: An IoT-based beehive monitoring system. Comput. Electron. Agric. 2021, 190, 106427.
  63. Debauche, O.; Mahmoudi, S.; Manneback, P.; Lebeau, F. Cloud and distributed architectures for data management in agriculture 4.0: Review and future trends. J. King Saud Univ.-Comput. Inf. Sci. 2021, 34, 7494–7514.
  64. Villa-Henriksen, A.; Edwards, G.T.; Pesonen, L.A.; Green, O.; Sørensen, C.A.G. Internet of Things in arable farming: Implementation, applications, challenges and potential. Biosyst. Eng. 2020, 191, 60–84.
  65. Bongomin, O.; Yemane, A.; Kembabazi, B.; Malanda, C.; Mwape, M.C.; Mpofu, N.S.; Tigalana, D. Industry 4.0 disruption and its neologisms in major industrial sectors: A state of the art. J. Eng. 2020, 2020, 8090521.
  66. Singh, R.K.; Berkvens, R.; Weyn, M. AgriFusion: An architecture for IoT and emerging technologies based on a precision agriculture survey. IEEE Access 2021, 9, 136253–136283.
  67. Mao, H.; Zhou, L.; Ying, R.; Pan, D. Time Preferences and green agricultural technology adoption: Field evidence from rice farmers in China. Land Use Policy 2021, 109, 105627.
  68. Li, H.; Huang, D.; Ma, Q.; Qi, W.; Li, H. Factors influencing the technology adoption behaviours of litchi farmers in China. Sustainability 2019, 12, 271.
  69. Qian, L.; Lu, H.; Gao, Q.; Lu, H. Household-owned farm machinery vs. outsourced machinery services: The impact of agricultural mechanization on the land leasing behavior of relatively large-scale farmers in China. Land Use Policy 2022, 115, 106008.
  70. Zhang, K.; Li, Y.; Wei, H.; Zhang, L.; Li, F.-M.; Zhang, F. Conservation tillage or plastic film mulching? A comprehensive global meta-analysis based on maize yield and nitrogen use efficiency. Sci. Total Environ. 2022, 831, 154869.
  71. Yu, L.; Chen, C.; Niu, Z.; Gao, Y.; Yang, H.; Xue, Z. Risk aversion, cooperative membership and the adoption of green control techniques: Evidence from China. J. Clean. Prod. 2021, 279, 123288.
  72. Chen, M.; Cui, Y.; Jiang, S.; Forsell, N. Toward carbon neutrality before 2060: Trajectory and technical mitigation potential of non-CO2 greenhouse gas emissions from Chinese agriculture. J. Clean. Prod. 2022, 368, 133186.
  73. Gözütok, M.E. Designing Programmable Logic Controller for Data Acquisition and Control. Ph.D. Thesis, Izmir Institute of Technology (Turkey), Urla, Turkey, 2010.
  74. Searchinger, T.; Waite, R.; Hanson, C.; Ranganathan, J.; Dumas, P.; Matthews, E.; Klirs, C. Creating a Sustainable Food Future: A Menu of Solutions to Feed Nearly 10 Billion People by 2050. Final Report; WRI: Washington, DC, USA, 2019.
  75. Morawicki, R.O.; González, D.J.D. Focus: Nutrition and Food Science: Food Sustainability in the Context of Human Behavior. Yale J. Biol. Med. 2018, 91, 191.
  76. Fasolin, L.H.; Pereira, R.N.; Pinheiro, A.C.; Martins, J.T.; Andrade, C.; Ramos, O.; Vicente, A. Emergent food proteins–Towards sustainability, health and innovation. Food Res. Int. 2019, 125, 108586.
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Sairoel Amertet
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