In precision agriculture (PA), the internet of things (IoT) and wireless sensor networks (WSN) can be utilised to more effectively monitor crop fields and make quick choices. The sensors can be installed in crop fields to gather pertinent data, but doing so uses up some of their limited energy. The use of IoT and WSN for smart precision agriculture necessitates energy-efficient operations, location-aware sensors, and secure localization techniques. In this chapter, agricultural problems are identified using IoT and WSN technologies to rectify them. Pests, a lack of water supply, and leaf diseases can be identified for best solutions through pest identification and classification, soil and water conservation, and leaf issues. The integration of Arduino and various sensors is used in the IoT and WSN to solve the issues automatically. Securing energy conservation can be achieved through IoT and sensor systems using efficient programmes.
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PA is the science of employing more advanced wireless sensors and smart analytics to improve agricultural production and management aspects. It is a ground-breaking concept that has been applied all over the world to boost productivity, reduce labour costs, and guarantee optimal fertiliser and water management. In order to avoid interfering with agriculture activity above ground, WSNs must be deployed underground. They could be used to collect information on variables related to soil, such as nitrate content, moisture content, and other factors. PA makes considerable use of data and information to boost crop output, farming productivity, and agricultural production. PA is a modern approach to crop management and agricultural growth that enables farmers to maximise inputs like water and fertiliser to raise production, quality, and productivity. To aid farmers in managing their operations, a vast amount of data at high spatial resolution is needed, which saves resources and benefits society (Vijayakumar & Rosario, 2011).
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Field research can have far-reaching effects.
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Fostering a culture of inquiry and participation.
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The emphasis of suggestions should be on quantity, administration, knowledge, and spatial flexibility.
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Advanced results are produced by PA research and development.
By integrating contemporary technologies and lowering the quantity of conventional inputs needed to develop crops, PA seeks to boost agricultural yields and profitability. Farmers can plant crops in more effective patterns and move from point A to point B with more accuracy with the use of GPS devices and lasers. This will help them apply water more effectively and reduce farm effluent discharge. This might lower the cost of agriculture and increase the availability of food. Predictive analytics software uses PA as a technique for obtaining data on crop, soil, and ambient temperature variables to provide farmers with guidance on crop rotation, planting and harvesting schedules, and soil management (Boopathi et al., 2023; Kumara et al., 2023; Vanitha et al., 2023). The digital transformation of the agriculture sector is a top priority to address the numerous difficulties faced in the fields. In order to achieve fine-grid crop management, two strategies—PA and smart farming—that combine cutting-edge technology with conventional agricultural methods may lead in this direction. PA systems might provide farmers with insightful scientific data on their farms, enhancing their competitiveness and earnings. The agriculture industry almost in its entirety may gain from technological improvements (Zhang & Kovacs, 2012).
Time to first node death, time to half node death, time to Last node death, time to ten percent node death, etc. are used to measure the lifespan of WSNs. Clustering is used to divide the network into non-overlapping clusters with one CH in order to optimise economic value. The effectiveness of clustering depends on choosing CHs from among all nodes, which might result in an unbalanced grouping and energy loss, which causes nodes to fail sooner. There have been attempts to balance energy loss across the network, but none of these have been deemed ideal. The performance of present clustering methods is outperformed by fuzziness in network parameters, which is why clustering protocols are not modified based on application requirements (Anand et al., 2021; Li et al., 2008).