The main steps for agricultural practices include preparation of soil, sowing, adding manure, irrigation, harvesting, and storage. For this, one needs to develop modern tools and technologies that can improve production efficiency, product quality, schedule and monitoring the crops, fertilizer spraying, planting, which helps the farmers choose the suitable crop. Efficient techniques are used to analyze huge amount of data which provide real time information about emerging trends. Facilities like fertilizer requirement notifications, predictions on wind directions, satellite-based monitoring are sources of data. Analytics can be used to enable farmers to make decisions based on data. This chapter provides a review of existing work to study the impact of big data on the analysis of agriculture. Analytics creates many chances in the field of agriculture towards smart farming by using hardware, software. The emerging ability to use analytic methods for development promise to transform farming sector to facilitate the poverty reduction which helps to deal with humane crises and conflicts.
TopIntroduction
Agriculture is the backbone for most of the countries around the world. But it is facing many problems in the developing countries where farmers cannot afford modern technologies and tools.
In farming sector, there has been a digital revolution. Big Data is playing a vital role in the development where the machines are equipped with various kinds of sensors which measures data in their environment which is used for predicting machines behaviour.
It promises a level of precision, information storage, processing and analyzing which was not possible previously due to limitations in the technology.The use of information sets and latest digital tools for collecting, aggregating and analyzing is done by Big Data. Information is gathered based on agricultural equipments and farmers using data from large datasets and analytics to make farming decisions.
Big Data in agriculture is Electronic Farm Records which contains dampness content information, pH level information, soil temperatures maps and information, electrical conductivity maps and information, online networking posts, protection and yield related data.
Big Data addresses the problems in the society which includes the need of consumers, producers, business analysts, marketing agents and decision making. Generally, analysis is done on large volumes of data and used in decision making process.
At present, agricultural sector is at the initial phase of rendering Big Data services. Farmers must join data systems and share farm data. Various applications and techniques can be used to enhance the farms productivity along with reducing their use of inputs.
Smart Farming is a emerging concept towards farm management using modern technology to increase the quantity and quality of product using the required human labor.
Figure 1. Why Agriculture needs Big Data?
TopBackground
Coble et al. (2016) proposed “large, diverse, complex, longitudinal, and distributed data sets generated by click-through streams, email, instruments, online transactions, satellites, sensors, video, and all other sources digital available today and in the future. This method is supported by describing data in terms of volume, velocity, variability, and accuracy, with “volume” referring to the size of the data, “velocity” measuring data flow, “variability” indicating the lack of structure and finally “veracity” accuracy and reliability of data.
Stubbs suggests big data as it is used in agriculture is small in terms of data size and more with the combination of technology and advanced analytics that creates a new way of processing data in a very useful and timely manner.
Woodard and Verteramo - Chiu 2017 mentioned that the use of geo-spatial techniques could improve crop harvesting models and the price of plant insurance products. We expect the sector to find the use of these methods in a number of areas including environmental economics. Agricultural information policies are analyzed and modified with various latest analytical tools and techniques. Decisions based on data collected from accurate agricultural technologies are applied at the farm level and to inputs and equipment manufacturers. There is an impact of management processes on production outcomes and on the management of farm managers.
Fountas et al., 2006 stated that Precision Agriculture is a knowledgeable work that can be categorized by data collection, data analysis and decision-making. It helps to differentiate decisions according to the planning and strategic boundaries.