California Patent of the Month – July 2023
Maximizing crop yield and efficiency is a top priority for agricultural managers. To achieve this, they must navigate numerous variables that can significantly impact the outcome including irrigation, pesticide use, local climate and more. Root Applied Sciences, Inc., a company focused on bringing actionable information to agricultural managers everywhere, has developed an automated and predictive agricultural management solution.
This tool involves the collection and assimilation of various data sets to train a predictive model for crop management. The first data set is obtained from a remote data collection service, providing information about global agricultural factors. Simultaneously, a second data set is collected from a local data collection device, focusing on local agricultural factors. These two data sets are then combined to create a comprehensive training data set, which serves as the foundation for the predictive model.
The trained predictive model generates measurable outcomes for crops based on the collected data. To further enhance the accuracy of predictions, a third data set about global vineyard factors and a fourth data set about local vineyard factors are collected. These additional data sets are analyzed by the prediction engine, which utilizes the trained model to predict measurable crop outcomes.
The predictive outputs of this advanced system are diverse and invaluable for agricultural managers. They include habitat suitability, suggested varietals to plant, expected crop quality, potential plantable crop varietals, and much more. By harnessing the power of predictive modeling and data analysis, agricultural managers can make informed decisions regarding irrigation, pesticide use, harvest dates, and other critical factors that influence crop yield and efficiency.
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