Agricultural pruning system

3:

• The agricultural pruning system of claim 1, wherein
• the first trained AI model is trained based on sensor data of a fruit tree that use a first cultivation method, and the first tag information includes first cultivation method tag information that indicates the first cultivation method;
• and
• the second trained AI model is trained based on sensor data of a fruit tree that use a second cultivation method different from the first cultivation method, and the second tag information includes second cultivation method tag information that indicates the second cultivation method;
• and
• the processor is configured or programmed to select the first trained AI model or the second trained AI model based on a comparison between desired cultivation method tag information and each of the first cultivation method tag information and the second cultivation method tag information.

4:

• The agricultural pruning system of claim 1, wherein
• the first trained AI model is trained based on training data points that include a measurement value concerning a first set of one or more cane attributes, and the first tag information indicates the first set of one or more cane attributes;
• the second trained AI model is trained based on training data points that include a measurement value concerning a second set of one or more cane attributes different from the first set of one or more cane attributes, and the second tag information indicates the second set of one or more cane attributes;
• and
• the processor is configured or programmed to select the first trained AI model or the second trained AI model based on the first tag information or the second tag information.

5:

• The agricultural pruning system of claim 4, wherein
• the first set of one or more cane attributes have different evaluation criteria depending on a cultivation method;
• and
• the second set of one or more cane attributes have unchanging evaluation criteria irrespective of a cultivation method.

6:

• The agricultural pruning system of claim 1, wherein
• the first trained AI model is trained based on sensor data of a fruit tree located in a first geographical region, and the first tag information indicates the first geographical region;
• the second trained AI model is trained based on sensor data of a fruit tree located in a second geographical region different from the first geographical region, and the second tag information indicates the second geographical region;
• and
• the processor is configured or programmed to select the first trained AI model or the second trained AI model based on the first tag information or the second tag information.

7:

• The agricultural pruning system of claim 1, wherein
• the first trained AI model is trained based on sensor data of a fruit tree of a first variety, and the first tag information indicates the first variety;
• the second trained AI model is trained based on sensor data of a fruit tree of a second variety different from the first variety, and the second tag information indicates the second variety;
• and
• the processor is configured or programmed to select the first trained AI model or the second trained AI model based on the first tag information or the second tag information.

8:

• An agricultural pruning system comprising:
• a storage to store a plurality of trained artificial intelligence (AI) models;
• and
• a processor configured or programmed to select a trained AI model from among the plurality of trained AI models;
• wherein
• each of the plurality of trained AI models determines whether to remove or retain each of one or more canes of a fruit tree;
• the plurality of trained AI models includes a first trained AI model and a second trained AI model;
• the first trained AI model is trained based on sensor data of a fruit tree grown in a field of a first predetermined size range, and the first trained AI model is associated with first tag information that indicates the first predetermined size range;
• and
• the second trained AI model is trained based on sensor data of a fruit tree grown in a field of a second predetermined size range different from the first predetermined size range, and the second trained AI model is associated with second tag information that indicates the second predetermined size range;
• and
• the processor is configured or programmed to select the first trained AI model or the second trained AI model based on a comparison between desired tag information and each of the first tag information that indicates the first predetermined size range and the second tag information that indicates the second predetermined size range;
• and
• the desired tag information includes a desired size range of a field.

13:

• The agricultural pruning system of claim 1, wherein
• the processor is configured or programmed to determine whether the first trained AI model or the second trained AI model meets an evaluation threshold;
• and
• the processor is configured or programmed to determine whether or not to select the first trained AI model or the second trained AI model based on whether or not the first trained AI model or the second trained AI model meets the evaluation threshold.