✓

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 2 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 [31,32,33,34,35,36].

Figure 3 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 [36,37,38,39,40].

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 [41,42,43,44].

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 [45,46,47,48,49].

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 [50,51,52,53,54].

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 [55,56,57,58,59].

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 [60,61,62].

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 [63,64,65,66].

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 [67,68,69].

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 [70]. 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 [71,72,73].

Tractors that combine GIS-based terrain mapping can be used for a range of field operations, from cultivation to harvesting. These autonomous tractors [18] have a 3D laser scanner, GPS-enabled cameras, and other multiple sensors that detect various parameters, such as terrain and weather conditions [74].

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 [75,76].

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 [77,78].

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 [79,80].

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 [81,82].

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 [83].

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 [84].

With the latest smartphone technology, farmers can now more easily and conveniently integrate farm automation technologies with remote monitoring from their smartphones and tablets [85].

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 [86].

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 [87].

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 [88].