Machine Learning Approach for Crop Maturity Detection and Automatic Sprinkler Irrigation Using IoT Technique

Abstract

Agriculture is the major source of income for majority of the citizens of  our country. The level of agriculture in India is decreased. The people who are dependent on farming should know use future technologies to solve difficulties which are shortages in water, excess expenditure on goods required for farming. This leads to huge financial loss for the farmers thus leading to the suicide. Because agriculture is such an essential issue, every technological breakthrough should be pursued. Agriculture's need has increased drastically as the world's population has grown, yet farmers are unable to supply the never-ending demand. Implementing smart or precision agriculture practices using IoT will be a better solution than expanding agricultural scale. The micro- and macronutrients in the soil have a direct impact on the quantity and quality of the yield, as well as the crop's health. Historically, human observation and judgement were employed to determine the health of the soil and crops. However, this technique is neither exact nor timely. This is accomplished by monitoring environmental factors that influence crop growth, such as temperature, humidity, soil moisture, and so on. It also aids farmers in determining which idle crop will perform best for them based on the information acquired and the environmental conditions. This strategy has the potential to be far more efficient than traditional techniques because it significantly minimizes the probability of crop failure, decreased yield, excessive water use, and other issues. The data collected by the sensor nodes scattered around the field is sent to the cloud, where it is analyzed and displayed for farmers to utilize. Numerous real-time applications highlight the wide spectrum of technology breakthroughs made in a variety of sectors.