Search

In an agriculture system major issues of irrigation systems for plants water supply is a critical factor. A significant amount of freshwater is required for this task but after the utilization of water in the irrigation process it is being polluted. In addition, the excessive use of water during the irrigation process can negatively affect crop production. Therefore, we need to provide a balanced amount of water for effective crop production and conservation of water. In this paper, we proposed low-cost irrigation planning with two key aims: first is to reduce the installation and maintenance costs of data collection in innovative irrigation systems and second is to control the valve for water supply automatically. In this context, we first provide a review of recent irrigation systems based on the Internet of Things (IoT) and Machine Learning (ML). Next, we introduce a working plan to collect crop water requirements using a soil moisture sensor. Then, an algorithm is proposed to decide the water supply for water treatment. Finally, the experiments are conducted on the samples collected from the farmland of wheat crops. Additionally, two different scenarios are considered to collect the water requirement samples. Based on the experimental and theoretical analysis of water requirements the proposed irrigation system can reduce the water demand by up to 25% as compared to traditional ways of irrigation. Moreover, in comparison of popular valve automation system the proposed multiple valve based system reduces the amount of water wastage up-to 22%. Therefore by utilizing the advance computational techniques (IoT and ML), we can reduce the cost of irrigation system and planning. [ABSTRACT FROM AUTHOR]

This article highlights the potential for collecting and processing territorial data in order to facilitate planning and programming that respond to real local problems and include the political and regulatory framework in force. A case study is explored that involves the joint use of two databases with institutional functions: the Farm Accountancy Data Network (FADN) and the National Information System for Water Management in Agriculture (SIGRIAN). Both databases are managed by the Council for Agricultural Research and Economics (CREA). Those data were used to calculate economic-structural indicators for irrigated and livestock farms located in the Po River Basin District and to run the socioeconomic analysis required to update the Water Management Plan. The updating of plans is governed by the Water Framework Directive (Directive 2000/60/EC), which establishes the community framework for water and requires all Member States to review and update their Plan every six years. The first update deadline was December 2015 and the second one will be December 2021. The integrated use of two databases made it possible to identify farms according to two types of irrigation: collective or self-supplied. With collective irrigation (Irrigation Water Service), the farm is a user of a Local Agency for Water Management (LAWM) that collects and distributes irrigation water. With self-supplied irrigation, the individual farmers collect and distribute water themself. The analysis carried out demonstrates the need and opportunity to develop coordinated data collection and management systems, thereby strengthening and refining the monitoring and programming of water use in line with the real needs of the territory.