Abstract
Agriculture is critical to human life. Agriculture provides a means of subsistence for a sizable portion of the world’s population. Additionally, it provides a large number of work opportunities for inhabitants. Many farmers prefer traditional farming approaches, which result in low yields. Agriculture and related industries are vital to the economy’s long-term growth and development. The primary issues in agricultural production include decision-making, crop selection, and supporting systems for crop yield enhancement. Agriculture forecasting is influenced by natural variables such as temperature, soil fertility, water volume, water quality, season, and crop prices. Growing advancements in agricultural automation have resulted in a flood of tools and apps for rapid knowledge acquisition. Mobile devices are rapidly being used by everyone, including farmers. This paper presents a framework for smart crop tracking and monitoring. Sensors, Internet of Things cameras, mobile applications, and big data analytics are all covered. The hardware consists of an Arduino Uno, a variety of sensors, and a Wi-Fi module. This strategy would result in the most effective use of energy and the smallest amount of agricultural waste possible.
Highlights
Agriculture is essential to the nation’s economy because it feeds the whole population
As a result of the vast variety of characteristics chosen, the results demonstrated that the systems had greater stability
Villacampa [16] compared feature selection approaches such as knowledge gain, correlation-based feature collection, relief-F, wrapping, and hybrid methods for reducing the number of features in data sets. ree general classification algorithms (Decision Trees, K-Nearest Neighbor, and Support Vector Machines) were used as classifiers to evaluate the efficiency of the aforementioned methods. e relief-F system outperformed all other methods of choosing functions, according to the data
Summary
Agriculture is essential to the nation’s economy because it feeds the whole population. Income levels will be many times higher than they are driving up food demand, in developing countries. As a result, these countries will be more conscious of their diet and food quality. Farmers have a rough time gathering soil nutrient statistics, water nutrient information, groundwater level, environmental conditions, and seasonal crop data concerning their farmland. They are having difficulty making better decisions based on the facts available to them. The use of the Internet of ings concept, machine learning, and cloud storage results in providing solutions to most of the problems
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