Agriculture carries greater risk than nearly any other sector. In agriculture, the proverb “you harvest what you sow” is not necessarily true. Because there is so much going on at the farm, it is very challenging for farmers to concentrate on all the everyday concerns such as weather, crop disease, commodity pricing, and fertilization schedules. In order to fulfil these rising needs, farmers must also produce more food with the limited water and land resources available due to the growing global population and food consumption. There will be a huge increase in the number of people to feed in 30 years; thus, agricultural methods must change to match the need. Researchers and scientists are now working to implement new IoT technologies in smart farming to assist farmers in creating better seeds, crop protection, and fertilizers using AI technology. Both the country's general economy and the profitability of farmers will benefit from this. This chapter discusses predictive agriculture, its need, advantages, challenges, and future of predictive analytics in agriculture.
TopPredictive Agriculture
About 12,000 years ago, the bare human eye and experience were crucial in farming. While examining the adoption of technology today, some paradigms continue to differ in terms of what is considered to be “up and coming” and what is generally acknowledged as a norm. Technology-driven tractors are being used where oxen and ploughs formerly ruled. Choices that once depended on the luck and experience of the farmer can now be supported by decades' worth of data, statistics, and predictive analytics.
Predictive analytics is one of the most fascinating technologies that is now being utilized, widely altered, and created (Dhanaraju et al., 2022). Plant breeders can more accurately estimate how to create a new variety that will thrive on that farm or in that environment as per the techniques scientists have developed.
Modeling, machine learning, and data mining are only a few of the many statistical techniques that make up predictive analytics as a whole. Science has only recently produced statistical procedures, measurements, and mathematical explanations for factors including weather, soil, wind, genetics, crop type, and physiology. A farmer must make predictions before planting crops.
Farming has always been a data-driven industry, according to Forbes. Farm economics, crop health, and weather are all rich sources of agricultural data. The Farmer's Almanac, which has been in print since 1818, provides long-term weather forecasts, calendars, details on full moon dates, and information on several other topics. One of America's first examples of reference agriculture data. The UN's Food and Agricultural Organization (FAO) estimates that in order to meet the anticipated demand, agricultural output would need to increase by 70% by the year 2050, when the world's population is predicted to surpass nine billion. The use of data analytics in agribusiness has significantly increased thanks to these factors as well as technological advancements.