System, apparatus and method of non-destructively determining petiole nutrient values in near real-time using a portable spectrophotometer

An apparatus for non-destructively determining petiole nutrient values in near real-time from plant leaves in a crop field, the apparatus comprising:

• a memory storing a machine learning model trained to provide petiole nutrient value outputs based on leaf spectral data inputs;
• and
• at least one processor communicatively coupled with the memory, the at least one processor configured to collectively:
• receive leaf spectral data generated by a portable spectrophotometer based on non-destructive spectral measurements of a leaf of a plant in the crop field;
• store the leaf spectral data in the memory;
• input the leaf spectral data into the machine learning model;
• receive an output comprising the petiole nutrient values determined by the machine learning model based on the leaf spectral data;
• and
• provide a near real-time result to a mobile device, the near real-time result indicating the petiole nutrient values.

A non-transitory computer-readable medium comprising instructions executable by a processor, wherein the instructions when executed configure the processor to:

• receive leaf spectral data generated by a portable spectrophotometer based on non-destructive spectral measurements of a leaf of a plant in a crop field;
• store the leaf spectral data in a memory;
• input the leaf spectral data into a machine learning model trained to provide petiole nutrient value outputs based on leaf spectral data inputs;
• receive an output comprising the petiole nutrient values determined by the machine learning model based on the leaf spectral data;
• and
• provide a near real-time result indicating the petiole nutrient values.

A system for non-destructively determining petiole nutrient values in near real-time from plant leaves in a crop field, the system comprising:

• a portable spectrophotometer configured to:
• generate leaf spectral data based on non-destructive spectral measurements of a leaf of a plant in the crop field;
• and
• transmit the leaf spectral data for receipt by a remote server via a network;
• and
• the remote server comprising a processor and a memory, the memory storing a machine learning model trained to provide petiole nutrient value outputs based on leaf spectral data inputs, the remote server configured to:
• receive the leaf spectral data;
• store the leaf spectral data in the memory;
• input the leaf spectral data into the machine learning model;
• receive the petiole nutrient values determined by the machine learning model based on the leaf spectral data;
• and
• provide a near real-time result indicating the petiole nutrient values to a mobile device.