Muthunpandian et al. (2017) proposed an automatic system for crop field monitoring continuously. The model maintains the water levels within the crop field. This developed device is useful in the irrigation system. Another computerized irrigation model was developed by Gutiérrez et al. (2014) to optimize water use in agricultural crops. The gadget has a distributed wireless community of soil-moisture and temperature sensors located inside the root sector of the plants. In addition, a gateway unit handles sensor facts, triggers actuators and transmits information to an internet application. Dwarkani et al. (2015) proposed a model for smart farming. They used smart sensing system and smart irrigator system with the help of wireless communication technology. In this model, a mechanical bridge slider arrangement is used on which smart irrigator moves. The smart irrigator receives signals with the help of sensors and global system for mobile communication (GSM) module. The crop details are analyzed by a centralized database and transferred to irrigator system to perform automatic actions. Sukumar et al. (2018) proposed a model for smart agriculture in which fertilizers are used effectively and it reduces wastage. This system is developed for monitoring crop field using sensors (temperature, soil moisture, humidity and light). The wireless transmission is used to send data received from sensors to a database. This system finds the moisture values from the sensor and turns the lights in the greenhouse ON and OFF based on light sensors and actuators. Mubarak et al. (2015) proposed an automated irrigation system which is very economical in terms of power consumption. This system can be implemented in large agricultural fields. With the help of GSM, user can control the motor from anywhere by just sending an SMS. The system is adaptable to manual mode also, if required. This system uses solar energy and it also works in all climate conditions for variety of crops. Fisher and Kebede (2010) proposed a system for monitoring soil moisture, and soil, air and canopy temperatures are measured in cropped fields. Various types of sensors were used in this system for continuous, automated monitoring of crop conditions. Kim et al. (2011) developed a system for water management based on wireless sensor networks and a weather station for internet monitoring of drainage water using distributed passive capillary wick-type lysimeters. Mirabella and Brischetto (2011) proposed a model in which farm is made up of several greenhouses. A multiprotocol bridge has been implemented using wireless system so that great flexibility can be provided. Wenshun et al. (2013) proposed a monitoring system for the use of fertilizers and pesticides in the field. Many techniques like sensor nodes, mining, wireless networks etc. are used for getting diseases information, so that necessary actions can be taken to save the crop. Wang and Liu (2014) proposed a model for cattle movement in the field using IoT. This system really helps farmers to save their cattle and also their time. Yang et al. (2013) proposed a system for CO2 monitoring on the surface of the soil based on various sensors. They also proposed a system for monitoring humidity, temperature and light intensity. One of the major requirements of smart farming is transmission of quick and reliable information to the farmers. Zhu et al. (2014) proposed a system in which data collected by sensors are transmitted to mobile users in a fast, reliable and secure manner. The system capacity is enhanced by means of data encryption and decryption techniques applied to cloud, mobile devices, sensors and cloud gateways. Pratama et al. (2019) developed a system for monitoring condition of cattle in real time and to facilitate farmers in terms of monitoring. Various types of sensors for reading body temperature, heart beat rate and movement of cattle are used for collecting data. Based on these data, normal, less normal and abnormal health classification of cattle is done. Nukala et al. (2016) discussed the use of IoT technologies in the food supply chain management. This supply chain includes everything from farm to fork like agriculture, food processing, transportation, distribution etc. They discussed various technologies like radio frequency identification, wireless sensor networks, cloud computing and data analytics for maintaining food supply chain so that fresh fruits and vegetables can be delivered. The main factor which limits the productivity of crop yield is drought. Remote sensing is used in rural areas to obtain frequent soil moisture data, which help to analyze the agricultural drought in distant regions. The soil water deficit index is calculated by Martínez et al. (2016). Vagen et al. (2016) used the moderate-resolution imaging spectro-radiometer sensor to map various soil functional properties to estimate land degradation risk. Santhi et al. (2017) developed sensor- and vision-based autonomous robot called Agribot for sowing seeds. The robot is enabled with GPS system to perform on any agricultural land. Further, Karimi et al. (2017) proposed non-contact sensing method to determine the seed flow rate. They used sensors with LEDs and signal information linked to the passing seeds to measure the seed flow rate. Cuhac et al. (2012) utilized light ward resistors as collector parts for seed stream estimation. They introduced a real-time wireless seed observing framework for seed drill executions. The light transmitting diodes and light ward resistors were introduced on each channel to determine the seed flow with the help of seed counting information. Recently, drones are playing key roles to assist farmers in some areas like soil and field analysis, planting, crop monitoring, irrigation, plant counting and gap detection, spraying the pesticides and detection of plant species etc. (D’Oleire-Oltmanns et al., 2012; Romero-Trigueros et al., 2017; Gnädinger & Schmidhalter, 2017; Faiçal et al., 2017; Hunter et al., 2017).
Farming is important for our survival. It is essential for the growth of a country’s economy. It furthermore gives adequate work opportunities to many people. Numerous farmers are still using conventional systems for agricultural practices which result in low yields. Nevertheless, with the advent of computerization customized equipment is left behind, and the yield is improved. So, use of computerized technology is required in the cultivation sector for extending the yield. A large number of papers propose the use of remote sensors for recording data in a brief timeframe to send it to server for processing. The gathered information provides various characteristic elements on which decision-making is done (Anusha et al., 2019).
Monitoring only environmental factors is not adequate for improving the yield of the harvests. There are various components that impact the yield. These components include attack of bugs, etc. There is a likelihood of theft at the time of harvesting. So as to offer responses to each and every such issue, it is imperative to make a facilitated structure which will manage all segments impacting the productivity at each stage like advancement, assembling and post-gathering storing. A system for watching the field is thus required. With IoT, related gadgets have penetrated into our lives, from prosperity and health, home motorization, vehicle and coordination to urban networks. IoT, related devices and automation have found its application in agribusiness. Development has seen different mechanical changes in the latest decades, getting progressively industrialized and advanced. By using distinctive agribusiness inventions, farmers have managed the route toward raising tamed animals and creating yields.
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