Use Cases of Artificial Intelligence in Agriculture

Artificial intelligence in agriculture

The agricultural practices over the decades have seen a remarkable transformation. Innovations of information technology and, particularly, artificial intelligence in agriculture are ensuring that agriculture sector provides impressive business opportunities. Volume of production, ease of monitoring, cost of farming, and operational comfort are some of the benefits that AI brings in agriculture.

Following are four of the most notable use cases of artificial intelligence in agriculture.

    1. AI for Harvest Improvement

    The use of excessive herbicides and pesticides do not only reduce the production volume of crops but also affect quality. Therefore, plant pathologists are leveraging the power of AI technology to optimize production by controlling the amount of chemical substances. Consequently, the quality of harvest is significantly enhanced over the past few years.

    To achieve this feat, IoT-based devices aid AI software to decide the areas which require the use of herbicides. Besides, this combination of software systems also assists in suggesting the precise quantity of herbicides above which the yield would start to decline.

    2. Robotics in Agriculture for Soil Quality Tracking

    The assistance from drone cameras powered by computer vision technology allows farmers to identify regions in farm which need attention. This technology enables farmers to perform surveillance of area in real-time. Besides, drones and the associated technologies reduce the amount of time required to monitor entire land area.

    The statistics below indicate that number of people employed in agriculture is decreasing rapidly owing to successive industrial revolutions over the decades. Before the requisite technologies, farmers had a hard time coping up with this change.

    Fortunately, research and development in robotics have changed the circumstances in favor of agriculture. Now, the producers can attain much more ambitious production targets in the same land area with a fraction of workforce.

    3. AI-based Risk Analytics

    The efficacy of risk assessment defines the quality of farming. You need to identify all the possibilities leading to losses and the potential volume of loss in each case. Former is the quantitative risk assessment whereas latter is qualitative one.

    Role of artificial intelligence in agriculture highlights the most while crafting and executing a risk assessment strategy. The AI systems assist in predicting weather months ahead. This ability comes in handy in areas which are more likely to receive excessive rainfall and subsequent floods. The land owners can plan if the flood is likely or not and if it carries the potential or not to inflict damages to crops.

    4. Predictive Analytics for Aggregate Planning

    The utilization of data analytics is significant in contemporary agricultural practices. Data analytics enable farmers to make effective aggregate planning. In essence, farmers can plan the amount of production which should be stored after milling and the amount which requires to be sent to retailers immediately.

    Most of the crops cannot be stored for longer periods without spending excessive money on expensive storage schemes. Artificial intelligence in the field of agriculture assists in developing an appropriate balance between demand and supply. Farmers often find it challenging to deal with task practices that they have not performed earlier. AI chatbots are addressing these challenges effectively by providing instant assistance to farmers’ queries.

Artificial intelligence is becoming an essential component in modern farming. Farmers are able to attain significant advantages by reducing costs and increasing yield. Would you like to incorporate AI systems in your farming business as well? Contact us so that one of our experts will develop business use cases specific to your business.