Abstract
India is an agricultural country. Agriculture and its allied activities act as main source of livelihood for more than 80 percent population of rural India. Available irrigation systems are not efficient and lead to wastage of water. Literature revels that there is need to develop automated irrigation system with latest technology. In this paper we are proposing a system which will detect the moisture percentage of the respective farmland and compare it with the provided set point. Based on the difference our machine learning algorithms will decide how long the water pump will remain switched on, it will then close the pump after that particular time and give out final moisture percentage reading and final water level of the water storage tank. Also, there will be a website provided which will continuously show current moisture percentage, amount of water present in the tank, how long water in the tank will last, the usage statistics and predict preferred and non- preferred crop to grow in that particular season. Plus a manual override will be provided for all systems.Usage statistics will consist of a graph showing water usage vs day. System display will show amount of water used vs date and below that the median and mode of the outcomes will also be indicated. Proposed in-house designed system has potential to provide the list of preferred and non-preferred crops.
Highlights
Water is one of the precious natural resources which is depleting severely in India
Data Science/ Machine Learning is one of such new techniques which can help our country to reduce the overall impact of faulty water management in agricultural sector
Machine learning technology plays a vital role in the system
Summary
Water is one of the precious natural resources which is depleting severely in India. The past few decades we have witnessed a significant increase in demand for water across various sectors, facing the worst water crisis in history. It is a data analytic technique which has different types of algorithms and models to learn information directly from data It optimizes water usage and provides essential amount of water and fertility to the field and improves yield production, reduces manual intervention (man hours), and suggests or predicts preferred crop based on availability of water and available data. We will learn in detail the learning process of machine learning in the system Following this we will study the implementation of the model which provides insight on practical application on the system on a pre-decided test crop and the use case for this proposed system. Since Raspberry Pi only has digital pins and we need an output in analog, we are using an ADC (Analog to Digital Convertor)
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