Big Data is defined as data sets that are so large and complex that traditional data processing applications are inadequate. AI refers to the ability of a digital computer or computer-controlled robot to perform tasks that are characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from experience. Machines or robots can be trained to accomplish specific tasks by processing large amounts of data. Big Data and Artificial Intelligence (AI) technologies are playing an increasingly crucial role in agriculture as the amount of data collected with the help of sensors on and about farms is increasing. With a multitude of sources, AI increases the value of data being collected by analyzing and converting it into information to support farm management decision-making. The applications of big data and AI vary from relatively simple feedback mechanisms (e.g. a thermostat regulating greenhouse air temperature) to complex algorithms that provide growers with timely decision support (e.g. recommendations on crop protection strategy), or prescriptively and proactively implement automated management interventions (e.g., robotic weed control). The integration of multiple sources of data such as weather, market data, agronomic data, or benchmarks with other farms further enhances the effectiveness of this technology.

Big Data is required to enable AI and both technologies are integral to each other’s success. The applications in agriculture offer huge potential for the industry. It can be applied at a range of scales from converting data collected on individual animals and plants, to a whole farm level by presenting information for crop planning and monitoring. Few examples of the application of  these technologies in agriculture are as follows:

• Development of new plant seeds: Breeding has always been a data-driven enterprise. Advancements in big data have given rise to a new type of plant breeding technique called advanced predictive breeding. It enables the geneticist to crunch exponentially greater amounts of information (Big Data) and produce increasingly useful outputs – including predictions. For example, instead of crossing two plants and then executing field tests with 100 of their offspring, the resources can be concentrated on the 20 best candidates by discarding 80 of the offspring, thus saving both time and money.

• Precision Farming: AI is used to monitor yields, control equipment, access field conditions, and use inputs at exact levels across fields which can substantially increase productivity and profitability. Machine learning models are being developed to track and predict various environmental impacts on crop yield such as weather changes.

• Animal welfare: Big Data and AI have the potential to help farmers manage their livestock efficiently with minimum supervision. With the pressure to increase the yield of animal farms, the emphasis on the inclusion of AI in animal farming has been increasing. At present, AI is used in monitoring, forecasting, as well as optimizing the farm animal growth. Tackling parasites, biosecurity, and farm management are some of the thrust areas in the livestock industry where the use of AI technology can pay rich dividends. AI can also be used to study the pattern of the disease spread, to predict the geographical regions it will spread to and allowing farmers, veterinarians, and pharmaceutical companies to be better prepared for it. AI already is used in automated milking units that can analyze the milk quality and flag for abnormalities.

• Reduced operating costs: With the help of Big data and AI, the farmers can reduce operational costs via targeted allocation of inputs such as fertilizers and pesticides.

The various examples of Big Data and AI in agriculture are:

1. Blue River Technology are building smart farm machines to manage crops at a plant-level. The award-winning see and spray equipment catered by the company uses computer vision and machine learning to spray herbicides and control weeds. The machines see every plant and determine the appropriate treatment for each. The intelligent models developed by the company use computer vision and machine learning which can distinguish subtle differences between cotton plants and weeds of many species and sizes. The precise application allows growers to reduce the application of herbicides to a great extent and unlocks the ability to use herbicide alternatives to effectively control weeds that would otherwise be resistant. However, the equipment currently is operational on Cotton crops only. The company claims that its precision technology eliminates 80% of the volume of chemicals normally sprayed on crops and reduce herbicide expenditures by 90%.
2. Trade Genomics provides soil analysis services to farmers. The company developed the system which uses machine learning to provide an in-depth summary of their soils contents, such as pathogen screening focused on bacteria and fungi as well as a comprehensive microbial evaluation. The emphasis is on preventing defective crops and optimizing the potential for healthy crop production.
3. VineView is the leading provider of crop diagnostics for vineyards. The company aims to help users improve their crop yield and reduce costs. The company caters drone technology to vineyards to deliver innovative, custom data solutions that assist in crop uniformity optimization, irrigation management, harvest planning, disease mapping, and much more.? Once the drone completes its route, users can transfer a USB drive from the drone to a computer and upload the captured data to a cloud drive.  Algorithms are used to integrate and analyze the captured images and data to provide a detailed report on the health of the vineyard, specifically the condition of grapevine leaves.
4. aWhere, a company based in U.S. Colorado delivers weather-based agricultural intelligence to farmers, companies, development agencies, and governments. The company uses machine learning algorithms in connection with satellites to predict the weather, analyze crop sustainability, and evaluate farms for the presence of diseases and pests. Weather-based intelligence is delivered to enable informed decisions corresponding to increased weather variability. The company has over 1.9 million virtual weather stations. The daily weather predictions, are customized based on the needs of each client and range from hyperlocal to global.