Data Science in the Indian Agriculture Industry|MBA in operations management in Bangalore

Posted by Anil Kumar R On 27/12/2021 10:22:47

"Industry 4.0" refers to the concept of factories equipped with wireless connectivity and sensors and connected to a system capable of visualising the entire production line and making choices on its own. Industry 4.0 is built on a number of pillars, including big data, data analysis, IoT's smart sensors, cyber-physical systems, and 3D printing. With the emergence of low-cost sensors and the internet of things, big data is emerging as a critical component that enables the automatic acquisition of insights, knowledge, and decision-making for complex linked systems. It refers to large-scale data structures and enables the development of tools capable of meeting new needs for data volume, velocity, and variability management. It is defined by the five V's: volume, variety, speed, veracity, and value. The agricultural sector can improve its efficiency and focus on the changing needs of the present consumer by using Industry 4.0 matrix technology.

Agriculture is the process of cultivating plants and various crops for the purpose of producing items such as food, fabric, medicines, and a variety of other goods necessary for the maintenance and improvement of human life. Among these, crop farming is the most critical because it produces a variety of key human consumables such as rice, wheat, and sugar. The demand for such products has expanded linearly in lockstep with the worldwide population expansion. Intensive agriculture requires increased inputs and market integration, which raises the risk associated with each unscientific management decision made in agriculture. Historically, common sense-based decision-making and advocated blanket use of inputs have been prevalent in the agricultural sector. MBA in operations management in Bangalore

However, as big data gathering and processing technologies progress, we are moving toward a future of automated algorithmic data-driven decision making and smart farming. Faced with significant climate change and a growing global population, we must modify farming practices and crop selection swiftly. The solution to this is the Internet of Things, which consists of several sensors gathering large amounts of data and automatically analysing it, allowing farmers to obtain insight into which crops they may cultivate; in the meantime, the system monitors crop cultivation behaviour. Additionally, big data is giving impetus for the transition from precision agriculture to a future of smart agriculture.

Big data is also driving the development of a novel multidisciplinary approach for smart agriculture that leverages the Internet of Things (IoT), sensors, cloud computing, and mobile computing. As a result, I have discussed the applicability of Big Data in agriculture in this work. I've compiled a list of works, analysed the Big Data framework's operation, and highlighted its shortcomings. My primary contribution is to provide concise information regarding high-performance big data-based solutions for agricultural productivity decision making and information processing.

Agriculture's implementation of big data Agriculture production systems are influenced by a complex number of factors, for which we require a diverse and vast amount of datasets, or in a single term, Big Data. Agriculture has a variety of applications, including the following:

•Identifying and Managing Crop Diseases 

•More Intelligent Agricultural Practices 

•Visualization of the environment and crop state

 •Visualization of production and sales

 •Optimal farming decisions 

•Commodity prices 

•Crop recommendations

 •Hybrid crop selection

 •Pesticide suggestion

 •Farming practises 

Thus, Big Data can automate the monitoring, forecasting, and decision-making processes across all facets of agriculture, from crop cultivation to marketing.

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