Abstract: Systems and methods for predictive management of plants. Agricultural (and natural resource) managers may have a multitude of data sets and data sources available, but often lack a meaningful or proven way to assimilate all available data and then conclusively select actions. For example, a vineyard manager may be able to collect data about local and regional weather, precipitation, disease prevalence, insect prevalence, pesticide use, crop varietal, cover crop selection and many other inputs to a predictive machine-learning vineyard management engine. As all this data is collected through local devices and third-party services, a prediction model may be used to determine specific outcomes or recommended actions based on the trained predictive model. For example, the model may be used to predict optimal harvest date, disease spread and vector spread, pest spread and impact, best pesticide use, irrigation plans, fruit quality, and the like.

Abstract: Systems and methods for predictive management of plants. Agricultural (and natural resource) managers may have a multitude of data sets and data sources available, but often lack a meaningful or proven way to assimilate all available data and then conclusively select actions. For example, a vineyard manager may be able to collect data about local and regional weather, precipitation, disease prevalence, insect prevalence, pesticide use, crop varietal, cover crop selection and many other inputs to a predictive machine-learning vineyard management engine. As all this data is collected through local devices and third-party services, a prediction model may be used to determine specific outcomes or recommended actions based on the trained predictive model. For example, the model may be used to predict optimal harvest date, disease spread and vector spread, pest spread and impact, best pesticide use, irrigation plans, fruit quality, and the like.

Abstract: Systems and methods for predictive management of plants. Agricultural (and natural resource) managers may have a multitude of data sets and data sources available, but often lack a meaningful or proven way to assimilate all available data and then conclusively select actions. For example, a vineyard manager may be able to collect data about local and regional weather, precipitation, disease prevalence, insect prevalence, pesticide use, crop varietal, cover crop selection and many other inputs to a predictive machine-learning vineyard management engine. As all this data is collected through local devices and third-party services, a prediction model may be used to determine specific outcomes or recommended actions based on the trained predictive model. For example, the model may be used to predict optimal harvest date, disease spread and vector spread, pest spread and impact, best pesticide use, irrigation plans, fruit quality, and the like.