Remote farm damage assessment system and method

A method for providing remote farm damage assessment on a mobile device, comprising:

• initiating a request to assess crop damage via a mobile device;
• downloading a guidance workflow from a second device;
• requesting that a user of the mobile device go to each of the damage assessment locales using the downloaded guidance workflow on the mobile device;
• capturing a first set of damage assessment images in accordance with guidance from the customized guidance workflow;
• determining whether any of the first set of damage assessment images is not acceptable for use for damage assessment by analyzing quality of each of the first set of damage assessment images;
• and
• transmitting the first set of damage assessment images that are determined to be acceptable for use to assess damage to the second device.

A system for providing remote farm damage assessment, comprising:

• a farm sector selection module configured to determine a set of damage assessment locales for damage assessment;
• a script engine configured to incorporate the set of damage assessment locales into a workflow, wherein the system is configured to send the workflow to a user device;
• a damage assessment system configured to:
• receive a first set of damage assessment images from the user device based on the workflow provided, wherein each of the first set of damage assessment images includes geolocation information and camera information;
• determining a damage assessment based on the first set of damage assessment images using a damage assessment machine learning model;
• and
• outputting a damage assessment indication including one or more of whether there is damage, a confidence level of assessing the damage, or a confidence level associated with the level of damage.

One or more non-transitory computer readable media having instructions stored thereon which, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• determining a set of damage assessment locales for damage assessment;
• incorporating the set of damage assessment locales into a workflow;
• providing the workflow to a user device;
• receiving a first set of damage assessment images from the user device based on the workflow provided, wherein each of the first set of damage assessment images includes geolocation information and camera information;
• determining a damage assessment based on the first set of damage assessment images using a damage assessment machine learning model;
• and
• outputting a damage assessment indication including one or more of whether there is damage, a confidence level of assessing the damage, or a confidence level associated with the level of damage.