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Agriculture has constantly improved with the introduction of various technologies, from motorized equipment to biotechnology. Following business trends, the agricultural industry is looking to maximize efficiency by turning to AI technologies. AI technology has been implemented to help yield healthier crops, reduce workloads, organize data and improve a wide range of tasks in this $5 trillion industry.
Data powers optimized farming
A modest estimate puts 75 million IoT agricultural devices in use by 2020 and 4.1 million daily data points per farm by 2050. The volume of data -- collected through technologies ranging from farm machinery to drone imagery -- is too abundant for humans to process. Farmers and agricultural technology workers are turning to AI to help analyze data points, thus enhancing value derived from these data sources.
Farms produce hundreds of thousands of data points on the ground each day. With the implementation of agricultural AI, farmers can now analyze weather conditions, temperature, water usage or soil conditions collected from their farm to inform decisions. AI technologies are helping determine the feasible crop choices or which hybrid seeds will increase profit and decrease waste.
In addition to ground data, computer vision and deep learning algorithms process data captured from drones and unmanned aircraft systems. AI combined with unmanned aircraft systems and drones can capture images of the entire farm and analyze the images in near-real time -- monitoring and analyzing soil health and condition of crops across the entire farm, as well as identifying problem areas.
AI and agricultural accuracy
Using AI systems to improve harvest quality and accuracy is a management style known as precision agriculture (PA). PA uses AI technology to aid in detecting diseases in plants, pests and poor plant nutrition on farms. AI sensors can detect and target weeds while deciding which herbicides to apply within the right buffer -- preventing overapplication of herbicides and herbicide resistance.
Farmers are using PA to improve agricultural accuracy by creating probabilistic models for seasonal forecasting. These models can look months ahead and use data collected to provide farmers with base predictions for most suitable crop varieties for the season, ideal planting times and locations. Agricultural AI technologies can then optimize farm management by basing decisions on predicted weather patterns during the coming season.
Seasonal forecasting models powered by AI are proving to be most valuable for small farms in developing countries where access to resources -- especially data collected from their farms -- is limited.
AI reduces farm workload
Traditionally, farms have needed many workers -- mostly seasonal -- to produce and harvest crops to stay productive. However, less people are entering the farming profession, due to the physical labor and high turnover rate of the job. Furthermore, most agricultural work utilizes a highly mobile migrant workforce, which presents challenges for a stable and predictable workforce.
AI solves critical farm labor challenges by augmenting or removing work and reducing the need for large numbers of workers. Agricultural AI bots are harvesting crops at a higher volume and faster pace than human laborers, more accurately identifying and eliminating weeds and reducing cost and risk. AI farmers present a permanent solution for the unpredictable and fluctuating agricultural workforce.
Additionally, farmers are taking advantage of chatbots for assistance to seek advice and recommendations on specific problems. Chatbots are already being used in numerous industries with great success, so it's no surprise that AI-powered chatbots should help farmers as well. Through the use of agricultural AI and cognitive technologies, farms across the world are able to run more efficiently to produce the fundamental staples of our dietary lifestyles.