Fertigation Management System Model Using Supervised Machine Learning and Time-Duration Method on Agricultural Industrial Land (original) (raw)
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IRJET, 2022
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Irrigation decision systems and water need models have been important research topics in agriculture since 90s. They improve the efficiency of crop yields, provide an appropriate use of water on the earth and so, prevent the water scarcity in some regions. In this paper, a comprehensive survey on water need models depending on crop growth and irrigation decision systems has been conducted based on machine learning and advanced control theory. The following outcomes and solutions are the main contributions. First, crop growth models and correspondingly water need models are suffer from un-modeled dynamics of the environment and lack of sensory devices. Second, irrigation decision systems based on the controller design are not fully efficient due to the imprecise crop growth models and time-varying environments. Third, water need models are depending on the inaccurate weather forecasts that also causes inefficient irrigation control. The relevant literature basis to these outcomes are...
Cropping pattern is a scheduling for farming time on a certain land in a definite period (e.g. 1 year), including unfilled area. In arranging crop planting patterns, hydrological (rainfall), climatological (temperature, humidity, wind speed, and sunshine), crop (crop coefficient value, productivity and price) and land area data are required. Therefore, a method that can be applied to predict the hydro climatological data is needed. The appropriate method for such prediction is Back Propagation Neural Network (BPNN). Prediction result of BPNN will be used to determine minimum crop water requirements, and it will be associated with planting time (age) of each crop for making cropping pattern. The design of most favorable cropping pattern will obtain the maximum profit and reduce fail harvest problem, which in turns it can contribute to national food resilience. Based on the simulation result, it was known that the BPNN with two hidden layers is able to predict hydro climatological data such as of rainfall, temperature, humidity, wind speed, and sunshine data with an average accuracy rate of 95.72%-96.61%. Meanwhile, validation of predictions obtained an average percentage error of 1.12% with an accuracy of 99.76%. The results of the optimization of the cropping pattern in Lombok in March 2013-February 2014 revealed an accurateness of profit in each district/city in East Lombok, Central Lombok, West Lombok, North Lombok, and Mataram increased 2.02%, 16.88%, 20, 23%, 21.89%, and 5.58%, respectively. Over all, the increasing average was found to be 13.3% from the previous year.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2023
Internet of things (IoT) smart technology enables new digital agriculture. Technology has become necessary to address today's challenges, and many sectors are automating their processes with the newest technologies. By maximizing fertiliser use to boost plant efficiency, smart agriculture, which is based on IoT technology, intends to assist producers and farmers in reducing waste while improving output. With IoT-based smart farming, farmers may better manage their animals, develop crops, save costs, and conserve resources. Climate monitoring, drought detection, agriculture and production, pollution distribution, and many more applications rely on the weather forecast. The accuracy of the forecast is determined by prior weather conditions across broad areas and over long periods. Machine learning algorithms can help us to build a model with proper accuracy. As a result, increasing the output on the limited acreage is important. IoT smart farming is a high-tech method that allows people to cultivate crops cleanly and sustainably. In agriculture, it is the use of current information and communication technologies.
International Journal of Advanced Research in Engineering and Technology , 2020
Moisture level of soil is one of the main factors in agriculture productivity and other organic applications. Moisture level in soil is not constant always and it varies with rainfall, temperature and other environmental factors. It is essential to measure the moisture level of soil to avoid drought condition of soil. Monitoring and measuring the moisture level in soil is tedious and error prone task when done manually and it is much time consuming but still results with less accuracy. In order to address this issue and improve the agriculture productivity and other ecological purpose machine learning based predictive modelling is proposed here for effective water resource management and drought control. Supervised learning kind of machine learning is adopted here which involves training and testing to predict the moisture level of soil. The training database is loaded with the values measured from the moisture sensor at regular intervals and sent via cloud. The data base must be loaded with necessary details such as date, moisture level, type of soil and the data from the database is used as test data to get the predicted response based on previously trained data