Smart Farming is the umbrella term used to denote the applications of the Internet of things and allied technologies in agriculture and farming. In addition to IoT, smart farming is based on actuators, sensor, robots, drones and geo-positioning systems. Smart farming is typically used to address water shortage, cost management, productivity, and lack of workforce needed for laborious and routine activities.
Smart Farming is expected to drive the third agricultural revolution. Techniques of plant breeding and genetics drove the first two agricultural revolutions.
An increase in agricultural production and a reduction in the cost of farming are the two main anticipated benefits of smart farming. These anticipated benefits are assured in smart farming through monitoring the environmental conditions, crop productivity, field management, soil and crop monitoring, movement of an unwanted object, attacks of wild animals, and thefts etc. Cost reduction and increase in productivity are achieved through optimal resource utilization and scheduling.
The process of smart farming is completely data-driven. It includes main four elements. These are:
* Physical Sensor world: These includes sensors for soil sensing, temperature sensing, weather sensing, light sensing, and moisture sensing. Similarly, devices perform many control functions like node discovery, device identification and naming services etc. They interact with either local onboard micro-controller or remote computer.
* Data Acquisition} A set of standard communication protocols are used to transfer the collected data to the processing node. Few examples of the protocols used are HTTP and RFID.
* Data Processing These steps include image processing, video processing, and extracting useful data for decision making.
* Data Analytics Data is mainly analyzed for two kinds of monitoring. First is livestock monitoring is performed through multiple sensors used to monitor different animal’s diseases like temperature, heart rate, digestion, etc. The second is field monitoring intending to report different fields like soil richness, temperature, humidity, gas, pressure air pressure and water pressure, and crop disease monitoring.
Use cases of Smart Farming:
Using these fundamental data processing activities, three kinds of high-level use cases are realized. These are described in the following sections.
1. Precision Farming
Smart farming includes the following set of activities.
* Climate Conditions monitoring The objective of this activity is to monitor weather conditions continuously so that future activities can be planned accordingly. This activity monitors parameters like temperature, humidity, wind direction, and air pressure etc.
* Soil Patterns This activity monitors soil parameters like soil humidity, moisture, fertilization and temperature. These parameters are monitored to identify the correlation between these parameters and a crop yield: soil humidity and moisture sensors. Using appropriate fertilization solutions can be recommended to the farmer.
* Pest and Crop Disease Monitoring, The underlying motivation of this activity is to reduce production losses caused by crop diseases. A proper monitoring system can be developed to predict plant diseases and pest attacks, animal attacks. The majority of plant diseases can be identified through image processing techniques, and proper remedies can be recommended.
* Irrigation Monitoring System The IoT based systems can be developed by monitoring weather and soil conditions. For this purpose, multiple sensors monitor physical and chemical constraints such as ph, temperature, conductivity and oxygen. Further machine learning algorithms are used to classify and predict water usage based on monitored parameter. Thus resulting in reducing farmers monthly irrigation cost and limiting water consumption.
* Determine the optimal time plant and harvest: IoT and other technologies like WSN and RFID can be used to determine the optimal time to plant and harvest.
* Tracking and Tracing The locations and positional technologies can be combined with other communication and sensor technologies such as WSN and Zigbee to support tracking and tracing operations. In such use-cases, soil, air, water, fertilizers, and pesticides conditions are monitored by RFID and Global Positioning System (GPS). Based on the remotely monitored parameters, the system gives an alarm to the farm manager when unwanted changes occur and help the farmers to take corrective action.
* Farm Management Systems Such a system integrates all farming-related activities to give the proper knowledge of fertilization, weather data, automatic buffer zone width monitoring, and automatic detail record generated according to the farms per day activities. The information stored in FMS can be accessed from anywhere at any time. Adoption of such a system results in optimized usage of resources and reduction in cost.
2. Livestock Management
The term livestock denotes domesticated animals raised for agricultural purposes. Animals like buffaloes, cows, goats, dogs, and chickens are a few examples. They are raised either for milk, egg, labour or security purposes. Raising such animals and protecting them against diseases and theft are the major challenges faced by farmers. The ability of reproduction of domesticated animals is also affected by the environmental condition. Hence, it is necessary to maintain an optimal environmental condition required for reproduction.
* Animal Health Monitoring Body temperature is one of the critical indicators for many diseases in animals. This can be monitored through IoT sensors to diagnose animal diseases.
* Heat Stress Level Cows's milk production depends on heat stress level. Increased heat stress is due to a lack of sufficient water level in the body. This may cause death in animals.
* Physical Gesture Recognition Physical gestures of animals can be monitored to analyze behaviour. This activity may include sleeping, walking, running etc.
* Rumination: Rumination in an animal is the process of chewing consumed food further. This process can be monitored by placing sensors on the nose. Rumination is also an indicator of animal health status.
* Heart Rate Heart rate monitors the stress level in animals. Heart rate another critical parameter useful to diagnose animal diseases.
3. Greenhouse Monitoring
Greenhouses are the places where plants are grown in a controlled environmental condition. Greenhouses are built using glasshouse technology. At such places, monitoring of the environmental condition is a significant activity. A combination of wireless sensor networks, IoT sensors, and remote data processing has effectively remotely monitored environmental conditions in greenhouses. The following activities are performed in greenhouses.
* Water Management An optimal water level needs to maintain for the proper growth of plants in greenhouses. An automatic drip irrigation system carries out plant watering in greenhouses. Here IoT sensors and data processing can be used to gauge soil moisture level required for plant watering.
* Plant Monitoring The plants in greenhouses need to be monitor regularly to check their growth. IoT sensors and cameras are used to automate such continuous monitoring and raise alarms in abnormal situations.
* Climate Monitoring IoT sensors can also monitor and maintain the optimal level of ventilation, temperature, carbon dioxide, and oxygen level in greenhouses.
