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5 Ways AI in Agriculture Will Help You Get More Business

Published Date : 2019-09-17 12:58:26
Agricultural industry accompanied the evolution of mankind from prehistoric times to present days and fulfilled reliably one of its most essential needs: food supply. Today this still remains its fundamental mission, however, it's integrated into a more complex mechanism driven by various sociological, monetary and environmental forces. 

This $5 trillion industry signifying 40 percent of employment, 10 percent of worldwide consumer spending, and 30 percent of greenhouse gas outflows continue to sustain the momentum with world's evolution, changing immensely over the previous years. Technological advancements are dominating the industry, upgrading food production while enhancing the overall farm-to-work supply chain and helping it use natural resources all the more efficiently.

Data generated by sensors or drones gathered at agricultural lands, on the field or amidst transportation offer an abundance of data about soil, seeds, domesticated animals, crops, equipment or the use of fertilizer and water. Technologies leveraging IoT and advanced analytics assist farmers in analysing real-time data like climate, temperature, dampness, costs or GPS signals and provide insights on the most proficient method to enhance and increase yield, enhance farm planning, settle on more intelligent choices about the level of resources required, when and where to disseminate them so as to reduce waste. 

Proficiency and profitability will increase in the coming years as 'precision farming' gains more reputation and farms become more astute and connected. It is assessed that by 2020, more than 75 million IoT equipment will be in use in agriculture, while the average farm will generate an estimated 4.1 million data points each day in 2050, all the way from 190.000 in 2014. 

While the ever-increasing number of connected devices means a big opportunity for players operating in the food and agriculture industry, it brings even more complexity to farms and companies alike. Besides, the abundance of unstructured data, similar to online content, drives the need to know more, to get real-time recommendations on devices at hand, such as smartphones or tablets. The answer? The use of cognitive technologies can assist in understanding, learning, reasoning, and collaborating and in this way, increasing efficiency.

Here are five ways AI in agriculture will help you get more business:
1. Cognitive IoT technologies 

With digitalization, the agriculture industry is getting disrupted and more data is being collected into the systems. To enhance value, the Watson IoT platform is providing solutions by applying ML capabilities to sensor or drone data, thereby transforming management systems in real AI systems. Cognitive IoT technologies allow numerous kinds of correlations of a large amount of organized and unorganized data from different sources, for example, online networking sites, soil information, historic weather data, research notes, commercial data, images, and so on, to extract knowledge and provide businesses more insights and proposals to make strategic decisions and improve yields.

2. Image recognition 

Agricultural drones help farmers in monitoring crops, taking a better look at the fields, seeding or analyzing plant health. Farming becomes more effective when IoT, drone data, and cognitive technologies unite to enhance strategies. As of late, unmanned aircraft systems manufacturer Aerialtronics cooperated with IBM to bring the IBM Watson IoT Platform and the Visual Recognition APIs to commercial drones so as to capture images, analyze them in real-time, identify issues, and take appropriate measures with regards to the issues. These systems are efficient, safe, and lessen potential human blunder while improving effectiveness. Agriculture could profit significantly out of it.

3. Agricultural skills and workforce

As per the latest World Urbanization Prospects report, the UN predicts that 66% of the total population will live in urban areas by 2050, ultimately leading to a decline of the workforce in the rural areas. Technologies using cognitive systems will help overcome this issue by easing the work of farmers, eliminating the need for an additional workforce to work the land. Numerous activities will be done remotely, processes will be robotized, risks will by identified and issues comprehended before occurring. In the coming years, the correct blend of abilities will likely technology and agricultural skills instead of pure agriculture.

4. Determine the best possibilities to maximize return on crops

The use of computer vision in agriculture could help determine the best choices in crops or hybrid seeds for a crop mix adapted to different conditions, objectives, and better suited for agricultural needs. By using its diverse ML capabilities, Watson can see how seeds react to various soil types, weather conditions, and local circumstances. By analyzing and comparing data about climate, types of seeds, soil or infestations in a specific area, information about what worked best, annual results, commercial trends, probability of diseases, costs or consumer needs, farmers can settle on the best choices to maximize return on crops.

5. Chatbots in agricultural need

Chatbots are conversational virtual assistants who interact with end-users via auditory or textual methods. AI-powered chatbots, using ML capabilities, understand natural language and interact with users in a customized manner. While it's still in the nascent stage and chatbots are used generally by retail, travel or media companies, agriculture could likewise use this emerging technology by helping farmers with providing appropriate solutions to their inquiries, offering guidance and suggestions on explicit agricultural problems.
                                
 

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