In traditional agriculture, a schedule is  predetermined considering factors such  as rainfall, suitable weather, etc. and all tasks are performed in order accordingly. Despite their efforts, farmers face difficulty in making proper  decisions due to lack of essential  information at appropriate time. Thus, it  is required to collect real time data on weather, air quality, and soil fertility  and even on availability of labour and essential capital investment in total and analytical predictions, to make ingenious decisions. This way of farming is known as precision agriculture.

Big data has emerged big in integrating various industries across the world  among which agriculture is a part too. Agriculture and other agri-businesses  require innumerable decisions to be taken based on various influencing  factors. Big data is now a driving factor for progress in precision agriculture  upon which farmers are relying, with the expectation of maximum yields.

The agricultural data can be classified as private data and public data. The  private big data set contains data obtained at the production level and  generated by an individual farmer. It mainly includes information regarding  ones farmer’s field, soil type, irrigation level, yield, livestock, etc. The public big data are at public level. There are funded agencies which collect,  maintain and analyse data records. The records may contain data about  weather conditions, soil survey, farm program participant records, marketing, etc.  

Big data in agriculture involves digital records of farm data. This includes  soil moisture content, temperature, weather variability pattern, irrigation facility, financial assistance like insurance and loans schemes, humidity level data, nutrient content data, historical cultivation data of the field and also knowledgeable articles on agriculture written by researchers and innovative agriculture practitioners. Global Positioning System(GPS)  and  Geographic Information System(GIS) have enabled to quantify the spatial  variability within fields. GPS allows collection of geo referenced data and  GIS makes spatial analysis and visualisation of interpolated maps. Data is collected via various sensors such as soil moisture sensors, microphone  sensor to detect pest using sound detecting technology and detection  algorithms, chemical/gaseous sensors to measure gaseous emission from  fields (like during ripening of  fruits, flower  pollination, etc.) and ultrasonic sensors to detect underground water availability for irrigation.  

The end users of the private as well as  public big data are farmers and  ranchers themselves. The resulting information of big data analysis can be  helpful in two ways, as a direct tool contributing to the agricultural  productivity and as an indirect tool to aid farmers in making well informed  and qualitative on farm decisions. The high resolution geographical maps  generated can be used to identify variability of soil in the terrain and the  quality measurement values help to decide the type of crop to cultivate  based on the nutrient richness of the soil. Historical data on weather pattern  are vital in planning different phases of the agricultural activity, especially in places where climatic conditions are unpredictable. Big data applications  can be further improvised and enhanced to procure more accurate results and can be made more economical which will entirely transform the sector by appreciating research activity.     

Precision agriculture with big data analytics could achieve lots of benefits. The farmers could achieve production benefits. The farmers could achieve maximum yields by investing less on farms inputs and are not subject to high risk of incurring loss. It could benefits to farm environment. The benefits of farm environment includes improved soil quality, water availability.  Reduced inputs, pesticides, fertilisers, water and energy often result in off farm environmental benefits. The rise in the use of big data and technological advances are making way for the expansion of business opportunities and establishments of new ones around agriculture. 

While using precision agriculture with big data analytics, statutes and  government organisations govern the public data and set standards for  quality. Statistically, pubic data is found more reliable than various private  data because of the common standards set.  Hence, they are viewed trusted  and authoritative. public data is kept confidential by the managing authorities. Often agencies anonymise data to protect individual’s identity. The technology along with collecting, storing and analysing data presents  transparency. The access to internet and open data allows equal use of public data.

We have made an attempt to bring out the significance of the role played by  big data analytics in precision agriculture which has radically changed the field of agriculture. Now farmers have to convert their farming benefits with the help of big data analytics.

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