An automated method for operating an automated seed sampling system having a seed loading station, a seed transport subsystem, and a seed sampling station generally includes sensing whether individual seeds are successfully isolated from a bulk of seeds at the seed loading station, and sensing whether the isolated seeds are properly positioned by the seed transport subsystem adjacent the seed sampling station in preparation for removing tissue from the isolated seeds. In some aspects, the method further includes analyzing the tissue removed from the seeds for presence or absence of at least one characteristic, and selecting seeds based on presence or absence of the at least one characteristic.

Techniques are described herein for using artificial intelligence to predict crop yields based on observational crop data. A method includes: obtaining a first digital image of at least one plant; segmenting the first digital image of the at least one plant to identify at least one seedpod in the first digital image; for each of the at least one seedpod in the first digital image: determining a color of the seedpod; determining a number of seeds in the seedpod; inferring, using one or more machine learning models, a moisture content of the seedpod based on the color of the seedpod; and estimating, based on the moisture content of the seedpod and the number of seeds in the seedpod, a weight of the seedpod; and predicting a crop yield based on the moisture content and the weight of each of the at least one seedpod.

Techniques are described herein for using artificial intelligence to predict crop yields based on observational crop data. A method includes: obtaining a first digital image of at least one plant; segmenting the first digital image of the at least one plant to identify at least one seedpod in the first digital image; for each of the at least one seedpod in the first digital image: determining a color of the seedpod; determining a number of seeds in the seedpod; inferring, using one or more machine learning models, a moisture content of the seedpod based on the color of the seedpod; and estimating, based on the moisture content of the seedpod and the number of seeds in the seedpod, a weight of the seedpod; and predicting a crop yield based on the moisture content and the weight of each of the at least one seedpod.

A seed sensor senses seeds on a row unit and generates a seed sensor signal. A number of planting characteristics, such as a seed orientation, seed slugging, delivery system wear, and seed abnormalities, can be detected based on the seed sensor signal. The planter can be controlled based on the detected planting characteristics.

A seed sensor senses seeds on a row unit and generates a seed sensor signal. A number of planting characteristics, such as a seed orientation, seed slugging, delivery system wear, and seed abnormalities, can be detected based on the seed sensor signal. The planter can be controlled based on the detected planting characteristics.

An apparatus and method for estimating a germination ability of a seed includes a terahertz signal source for emitting a terahertz signal towards the seed, a detector for detecting at least part of the terahertz signal having interacted with the seed, a scanner for moving the support relative to the terahertz signal to provide a scan of the seed, a data processing device for forming an image data from the detected terahertz signal as obtained for a plurality of positions during the scan of the seed, and a decision support system for providing an estimate of the germination ability from the image data. In an embodiment, the terahertz signal source is arranged for emitting a continuous or pulse wave signal, and the detector is arranged for detecting an amplitude and a phase of the terahertz signal having interacted with the seed. A signal representing an outcome of the estimation may control a separator to separate seeds according to their estimated germination ability.

The present invention discloses methods and devices for a germination profile indicator based on conductivity including: utilizing a target-profile correlation of a target profile of an API from a plant seed to a target germination stage, the target profile includes identifying characteristics of chemical components, and wherein the target germination stage relates to a progress of a seed-germination process of the target profile; utilizing a process-profile correlation of germination-process profiles during the seed-germination process to respective process germination stages, wherein each germination-process profile relates to extracted seed material during the respective process germination stage; comparing characteristics of the target profile to corresponding features in the germination-process profiles; selecting an optimal state of the respective process germination stage to maximize an API quantity; and cross-correlating the optimal state to a conductivity range for the seed in a soak water within a given temperature range to identify a temperature-dependent germination stopping condition.

The present invention discloses methods and devices for a germination profile indicator based on conductivity including: utilizing a target-profile correlation of a target profile of an API from a plant seed to a target germination stage, the target profile includes identifying characteristics of chemical components, and wherein the target germination stage relates to a progress of a seed-germination process of the target profile; utilizing a process-profile correlation of germination-process profiles during the seed-germination process to respective process germination stages, wherein each germination-process profile relates to extracted seed material during the respective process germination stage; comparing characteristics of the target profile to corresponding features in the germination-process profiles; selecting an optimal state of the respective process germination stage to maximize an API quantity; and cross-correlating the optimal state to a conductivity range for the seed in a soak water within a given temperature range to identify a temperature-dependent germination stopping condition.

A seed sensor senses seeds on a row unit and generates a seed sensor signal. A number of planting characteristics, such as a seed orientation, seed slugging, delivery system wear, and seed abnormalities, can be detected based on the seed sensor signal. The planter can be controlled based on the detected planting characteristics.

An automated method for sampling seeds generally includes removing material from a seed at an automated sampling station and then, after removing the material from the seed, detecting, by at least one sensor, movement of the seed out of the automated sampling station. The automated method may also include, after removing the material from the seed, transferring the seed from the automated sampling station to a seed container and transferring the material removed from the seed from the automated sampling station to a sample container.