A harvester monitoring system configured to determine one or more parameters associated with harvested items, the system comprising: a camera module having a field of view and configured to generate image data associated with the harvested items; a mounting bracket configured to secure the camera module to a harvester such that a conveyor of the harvester is within the field of view of the camera module; a location sub-system configured to determine and output location data representative of a geographical location of the harvester monitoring system; and a processing unit configured to receive the image data and the location data, to determine one or more parameters associated with the harvested items, and to record the one or more parameters in association with the location data on a computer readable medium.

Implementations described herein relate to methods, systems, apparatuses, and computer-readable media for computer-assisted and automatic harvest, sorting, pruning, and trimming of agricultural products. In one implementation, the agricultural product may be a hemp or cannabis product. In one implementation, the agricultural product is automatically pruned through use an artificial intelligence (AI)-generated cutting path. In one implementation, the agricultural product is supported by a clamping assembly. In one implementation, the agricultural product is imaged in a positive-pressure imaging assembly. In one implementation, the agricultural product is automatically trimmed in a positive pressure cutting assembly.

Implementations described herein relate to methods, systems, apparatuses, and computer-readable media for computer-assisted and automatic harvest, sorting, pruning, and trimming of agricultural products. In one implementation, the agricultural product may be a hemp or cannabis product. In one implementation, the agricultural product is automatically pruned through use an artificial intelligence (AI)-generated cutting path. In one implementation, the agricultural product is supported by a clamping assembly. In one implementation, the agricultural product is imaged in a positive-pressure imaging assembly. In one implementation, the agricultural product is automatically trimmed in a positive pressure cutting assembly.

A method includes receiving, by the treatment system, during operation in an agricultural environment, one or more images comprising one or more agricultural objects in the agricultural environment, identifying, in real-time, one or more objects of interest from the one or more agricultural objects by analyzing the one or more images, wherein the analyzing results in a first object being identified as belonging to one or more target objects and a second object being identified as not belonging to the one or more target objects, logging one or more results of the identification of each of the one or more objects of interest and a corresponding treatment decision; and activating the treatment mechanism to treat the one or more target objects.

A method performed by a treatment system disposed on a moving platform, the treatment system having one or more processors, a storage and a treatment mechanism, comprising: receiving one or more images of an environment in which the moving platform is operating; identifying, in real-time, a pose of the moving platform using sensor inputs; identifying one or more target objects by processing the one or more images using a machine learning (ML) algorithm; and controlling the treatment mechanism to treat the one or more target objects by orienting the treatment mechanism towards the one or more target objects at least partially based on the pose.

A method performed by a treatment system disposed on a moving platform, the treatment system having one or more processors, a storage and a treatment mechanism, comprising: receiving one or more images of an environment in which the moving platform is operating; identifying, in real-time, a pose of the moving platform using sensor inputs; identifying one or more target objects by processing the one or more images using a machine learning (ML) algorithm; and controlling the treatment mechanism to treat the one or more target objects by orienting the treatment mechanism towards the one or more target objects at least partially based on the pose.

A method includes receiving, by the treatment system, during operation in an agricultural environment, one or more images comprising one or more agricultural objects in the agricultural environment, identifying, in real-time, one or more objects of interest from the one or more agricultural objects by analyzing the one or more images, wherein the analyzing results in a first object being identified as belonging to one or more target objects and a second object being identified as not belonging to the one or more target objects, logging one or more results of the identification of each of the one or more objects of interest and a corresponding treatment decision; and activating the treatment mechanism to treat the one or more target objects.

Embodiments of systems and approaches for managing post-harvest crop quality and pests are described. Such a system may include a plurality of edge devices each comprising sensor components and collectively forming a mesh network, for measuring the local physical environment within stored crops and, for example, transmitting the measurements to a service from within the crop storage area. In certain embodiments, such a system may be used to manage post-harvest crops and storage areas—for example, approaches are described for determining fumigation treatment duration, determining phosphine dosage, determining heat treatment duration, and determining safe storage time for crops.

Embodiments of systems and approaches for managing post-harvest crop quality and pests are described. Such a system may include a plurality of edge devices each comprising sensor components and collectively forming a mesh network, for measuring the local physical environment within stored crops and, for example, transmitting the measurements to a service from within the crop storage area. In certain embodiments, such a system may be used to manage post-harvest crops and storage areas—for example, approaches are described for determining fumigation treatment duration, determining phosphine dosage, determining heat treatment duration, and determining safe storage time for crops.

A method includes receiving sensor inputs including one or more images comprising one or more agricultural objects; continuously performing a pose estimation of the treatment system based on sensor inputs that are time synchronized and fused; identifying the one or more agricultural objects as target objects; tracking the one or more agricultural objects identified by the analyzing; controlling an orientation of the treatment mechanism according to the pose estimation for targeting the one or more agricultural objects; and activating the treatment mechanism to treat the one or more agricultural objects according to the orientation.