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Irrigation scheduling improves the water use efficiency and focus on evapotranspiration (ET) estimation methods for understanding of spatial variations of ET. The primary objective of good irrigation scheduling is to apply the right amount of water at right time.
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Agricultural irrigation scheduling is becoming a very important managerial activity whose ultimate purpose is to achieve effective and efficient utilization of water.
#Literature Review Of Smart Irrigation System free#
Farmers required to predict the need of water for the crops, to confirm the data provided by agricultural weather stations or to get insight the free water surface evaporation in lakes or dams. In the early twentieth century, irrigation is the most crucial practice no doubt and needs effective utilization. presented a thorough investigation to evaluate the possibility of using Machine Learning models to identify plant diseases. The fundamental idea is that the DSS should serve as a farm management tool, supporting farm managers in making decisions on irrigation, whether to irrigate and, if so, which field with how much water. New technologies and knowledge can help in this complex decision-making. To overcome these challenges, the multivariate, complex, and unpredictable agricultural ecosystems must be well understood by continuously analyzing, measuring, and monitoring several physical aspects and phenomena. The major challenge in agriculture sustainability and dawdling is due to climate change therefore, every drop of freshwater needs to be utilize effectively and efficiently. Wheat and Maize are the most commonly cultivated crops and have high water consumption in Punjab, India. As a result, water conservation and precision agriculture are becoming vital issues in tropical climate areas. Recently, the achievement of the Green Revolution is endangered by a significant decline in water resources. Punjab has 97.95% highest gross irrigation of the total cropped area. Central Indian Punjab is well-known for its agricultural activities and has occupied a high percentage of the land area all over India, and its agricultural production mainly depends on irrigation. In India, the demand of water for the agriculture and industry sectors is continuously increasing to fulfill the needs of 1.366 billion people. However, the agriculture sector of India has been transformed via the effective deployment of Information and Communication Technologies (ICTs) in traditional to modern practices which provide various services (such as- IoT agriculture, smart water management, soil management, plant diseases, crop management, geo-spatial image and livestock monitoring). Even after six decades of planned development, agriculture has played an important role in the Indian economy. Arthur Keith said that the advancement of agriculture is the first major step for civilized life. It is a critical input for enhancing agricultural productivity. Water is the main limiting factor of agricultural development in semi-arid and arid climates. This study introduces the area of research, including a irrigation water management in smart agriculture, the crop water model requirement, and the methods of irrigation scheduling, decision support system, and research motivation. Moreover, water irrigation management need to rapidly adapt state-of-the-art using big data technologies and ICT information technologies with the focus of developing application based on analytical modeling approach. We examined how such developments can be leveraged to design and implement the next generation of data, models, analytics and decision support tools for agriculture irrigation water system.
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This paper aims to review the application of big data based decision support system framework for sustainable water irrigation management using intelligent learning approaches. For accurate estimation of requirement of water for a crop a strong modeling is required. It depends on various parameters related to climate, soil and weather conditions. Irrigation water management is a challenging task for sustainable agriculture. Big data analytics becomes a key technology to perform analysis of voluminous data. Agriculture analytics is a data intensive multidisciplinary problem. From last decade, Big data analytics and machine learning is a hotspot research area in the domain of agriculture.