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Table 2.3 illustrates the technology for controlling pest in smart agriculture.

Table 2.1 Hardware for smart farming.

Hardware	Figure	Application	Utilization
Moisture sensor		It can measure the percent of water in soil. It measure dielectric permittivity of soil.	Farmers get alert of the growing condition of the plants.
Humidity sensor		It can measure the accurate air humidity level.	Farmers get alert of the growth of leaf, change in photosynthesis level of the plants.
PH sensor		It can measure the ph value of soil. It determines which nutrient is available in the soil.	Farmers get alert of the particular crop to grow in the field to get best production in terms of quality and quantity.
Spoiled crop detection sensor		It can detect the disease in crop in early phase by detecting the chemical compound released by an infected plant.	Farmers get alert of quick and timely crop protection.
Fertility sensor		It can measure the fertility of the soil.	Farmers get alert of the quality of the soil and accordingly choose the crop to plant.
Climate sensor		It can provide climate condition by measuring the CO2 level in air. Also monitor any faulty condition.	Farmers get the data about the environment like wind speed, humidity etc.
Temperature sensor		It can measure the temperature of the soil up to a certain depth from the surface of the soil.	Farmers get alert of the change in absorption of soil nutrients by plants.
Pressure sensor		It can measure the air pressure in a particular area depending on which rainfall on that particular area can be guessed.	Farmers get alert of the higher or lower level of rainfall and accordingly plan to monitor the plants.
Microcontroller		It detects signal from other devices and respond according to the process.	Many works of farmers can be automated by it.

Table 2.2 Technology of monitoring soil, climate and crop.

Authors	Parameters	Technology	Advantage
Liqiang et al. [1] Zhua et al. [2] Jaishetty et al. [3]	Image Temperature Humidity	Wireless sensor network	Low power consumption
Keerthi.v et al. [4]	Light Temperature Humidity Soil moisture	Cloud Computing, IoT	Collection of periodic data, Online storage
Srisruthi et al. [5]	Temperature Humidity Soil moisture Fertility	Sensors	Energy efficient, Low maintenance cost, also controls supply of waters and fertilizers
Swarup et al. [6]	Temperature Humidity Soil moisture	Wireless protocol, Serial protocol, Microcontroller, Wireless Bluetooth module	Uses of gateway
Channe et al. [7]	PH value Humidity Temperature	Sensors, Cloud computing, IoT, MobileComputing, Big-Data Analysis	Increase in crop production rate
Satyanarayana et al.[8]	Temperature Humidity	Sensors, Zigbee protocol, GPS
Sakthipriya [9]	Crop leaf condition	Wireless Sensor network	Increase in production of rice plant
Kumar [10]	Temperature downfall	Sensors	The system can measure crop loss amount, Increase in crop production
Baggio [11]	Temperature Humidity	Sensors	Identify and reduce potato crop disease
Kavitha et al. [12]		Sensors	Automatic detection of fire accidents
ICT [13]	Climate condition Soil Fertility	Internet, Wireless communication, remote monitoring system, short messaging service	Collected climate, soil pattern, pest and disease change pattern help farmer to plan for the next crop plantation
Entekhabi et al. [14]	Radar and radiometer information	Optimization, minimization, Extensive Monte Carlo numerical simulations	Efficient soil moisture, roughness estimation
Nandurkar et al.[15]	Temperature Humidity	Sensors, GPS	With controlling temperature and humidity, detection of theft is also possible
Liqiang et al. [16]	Temperature Humidity Soil moisture	Green energy, sensors.	The motor have both facility auto and manual, Increase in productivity, Water management

Table 2.3 Technology of controlling pest.

Authors	Parameters	Technology	Advantage
Thulasi Priya et al. [17]	Image.	GSM, Image sensor, Aspic, Wireless sensor network	Farmers get aware of the pests inside the field.
Shalini et al.[18]		Real-time system.	Automatic pesticide sprayer in the field.
Baranwal et al. [19]	Image, sensed data.	Python scripts, sensors, Integrated electronic device, Image processing.	Identification of pest, detection of crop disease, Increase in production.
Kapoor et al. [20]	Image of lattice leaf.	Matlab, Image processing.	Determine pesticide or fertilizer which prevent growth of plant.
Rajan et al. [21] Sladojevic et al.[22]	Image.	Imageprocessing, Machine Learning Approach.	Detect pest, determine pesticide which prevent growth of plant. Also determine the disease in plant leaf.