Dynamic, variable,constantly changingthese are words often iterated inagricultural discussions. They are theantithesisto crop yield predictability, and with good reason. In the field of precision agriculture, where farmers and businesses attempt to improve margins and predict yields, nature’s rain, wind, humidity, storms, droughts, plagues, etc., act as impediments to generating accurate predictions. In a world where just-in-time is still too slow, predicting the future is key to staying alive and meeting demands.
Precision agriculture experts and data scientists have been modeling Ag predictions for the past two-plus decades with mixed results and less-than-ideal accuracy because of the variability of nature. Variability in predictive modeling can pose significant challenges that require careful consideration and management to ensure that the model is accurate and effective. Models have improved accuracy, and more recently, AI has made inroads to those improvements by addressing the key modeling factors of data and reprocessing.
AI-powered tools can help clean and preprocess data to ensure that it is of high quality and ready for use in predictive modeling. These tools can detect and correct errors, fill in missing values, and identify outliers that may skew results.These tools often require Ag experts and data scientists to rely on IT to build server and storage infrastructures and later maintain and support highly technical environments.
As the public cloud companies strive to attract more business, specializations in cloud-based IA for precision Ag have made strides to enhance modeling making it less IT driven and afford more control and independence to the Ag teams, the end-users.Other key business benefits of the clouds are:
Cloud computing-based AI can offer significant enhancement by addressing the challenges of variability in predictive modeling, including scalability, cost-effectiveness, accessibility, security, and integration.
Scalability: Cloud computing offers virtually unlimited computing resources, which can be scaled up or down as needed to accommodate the demands of the AI-powered predictive modeling process. This makes it possible to process large volumes of data quickly and efficiently and to train complex models that may not be feasible with traditional computing resources.
Cost-effectiveness: Cloud-based AI platforms can be cost-effective, as they eliminate the need for expensive on-premises computing infrastructure and hardware. This can enable businesses to access the benefits of AI without incurring the high costs associated with building and maintaining their own computing infrastructure. For proof of concepts (POC), no capital outlays purchasing servers, storage, etc., means any of the IT tools needed fora POC can be completely eliminated with a few clicks of the mouse without further costs.
Accessibility: Cloud-based AI platforms can be accessed from anywhere with an internet connection, making it easy for users to collaborate and share data and models. This can improve the efficiency of the predictive modeling process and enable teams to work more effectively together, regardless of their location.
Security: Cloud-based AI platforms can offer robust security features, such as encryption and access controls, to protect sensitive data used in the predictive modeling process. Cloud providers can also offer regular security updates and monitoring to ensure that the platform remains secure.
Integration: Cloud-based AI platforms can integrate with other tools and systems, making it easy to incorporate AI-powered predictive modeling into existing workflows and processes. This can enable businesses to extract more value from their data and make more informed decisions based on predictive insights. As Software as a Service (SaaS) providers meet demands for Ag, such as companies providing satellite images of vast Ag fields or rainfall data, integration via APIs simplifies obtaining key modeling building blocks without deploying thousands of IoTs and associated infrastructures.
Overall, cloud computing-based AI can offer significant enhancement by addressing the challenges of variability in predictive modeling, including scalability, cost-effectiveness, accessibility, security, and integration.
