Amesbury, MA, United States of America

Chris Moscardini

USPTO Granted Patents = 3 

 

Average Co-Inventor Count = 33.0

ph-index = 2

Forward Citations = 23(Granted Patents)


Company Filing History:


Years Active: 2022-2025

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3 patents (USPTO):Explore Patents

Title: Chris Moscardini: Innovator in Agricultural Technology

Introduction

Chris Moscardini is a notable inventor based in Amesbury, MA (US). He has made significant contributions to the field of agricultural technology, particularly through his innovative use of machine learning in farming practices. With a total of 3 patents, Moscardini is recognized for his efforts to optimize crop production and enhance agricultural efficiency.

Latest Patents

One of Moscardini's latest patents focuses on a crop prediction system that utilizes machine learning operations to predict crop production. This system identifies a set of farming operations that, if performed, can optimize crop yields. The crop prediction system employs models trained on geographic and agronomic information. Upon receiving a request from a grower, the system can access information about a specific portion of land, including its location, weather conditions, and soil composition. By applying one or more crop prediction models to this data, the system predicts crop production and recommends an optimized set of farming operations for the grower.

Career Highlights

Chris Moscardini is currently employed at Indigo Ag, Inc., where he continues to develop innovative solutions for the agricultural sector. His work is instrumental in bridging the gap between technology and farming, making significant strides in how crops are managed and produced.

Collaborations

Moscardini collaborates with several talented individuals in his field, including David Patrick Perry and Geoffrey Von Maltzahn. These partnerships enhance the development of cutting-edge agricultural technologies.

Conclusion

Chris Moscardini stands out as an influential inventor in the realm of agricultural technology. His innovative approaches to machine learning in farming are paving the way for more efficient and productive agricultural practices.

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