innov8.ag is partnered with Microsoft to enable growers to make informed on and off-farm management decisions based on machine learning and AI-based insights. Our solutions leverage the agronomist expertise of PhD researchers from land-grant universities and ag-centric colleges; pairing mutual data sources, big data capabilities and machine learning tools with grower expertise and experience.
Our current focus is permanent crops, as labor availability & efficiency is a significant industry challenge, often representing 60% of growers' costs. We're currently piloting a Smart Orchard project with the Washington Tree Fruit Research Commission & Washington State University, where we're applying our data aggregation & data science capabilities to provide growers with insights focused on the bottom line. We’re already starting to see opportunities for cost savings and environmental conservation that can benefit growers everywhere.
innov8.ag include's nano-climate measurements and notifications for agriculture, enabling growers to understand weather across their acreage, including potential impact and implications. This information informs growers in prioritizing and fine-tuning planned resourcing (labor, chemicals, water, and equipment) to manage the growing cycle. This can be as simple as showing wind state/predictions to more complex determinations such as when it's viable to spray crops or weeds. It also includes frost warning notifications so staff can activate warming systems. Even more complex predictions are possible from data-mined information that includes triggers on when to water or thin based on current and historical satellite imagery, soil moisture/temperature, and market pricing.
Data consolidation enables growers to better visualize their disparate data sets and to understand more value through data interrelation combined with data portability—enabling growers to enjoy more autonomy when dealing with suppliers.
Data repacking enables growers to reuse their consolidated data for reporting to government agencies, insurers, and tasking semi-autonomous equipment.
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