Artificial Intelligence System for In-Vivo, Real-Time Agriculture Optimization Driven by Low-Cost, Persistent Measurement of Plant-Light Interactions

摘要

Herein is described an electronic sensor and Artificial Intelligence (AI) system capable of optimizing agriculture processes in real-time. A number of wired or wireless optical, electrical, thermal, chemical, biological, or other sensors are deployed in a plant's locality, possibly for its full lifecycle. These sensors provide long-term, in vivo observations of both the plant's environment and the plant itself. A plant's interaction with light is measured by persistent, cloud-connected sensors, including a spectral reflectance sensor, Chlorophyll Fluorescence detector (ChFl), ChFl imager, and a near-IR imager. This sensor data enables the in-vivo characterization of a plant's health status and photosynthetic efficiency. A cloud-powered AI system uses machine learning algorithms to model the system, recommend agricultural process improvements, and identify abnormalities associated with plant disease or pests. As the invention gets enough sensor data across various environmental conditions, an ideal target process flow can be identified for a given plant genotype, phenotype, or other additional specificity.