In a method of measuring moisture in corn, an ear of corn is operatively coupled to a moisture meter and an amount of moisture is determined while the ear of corn is being grown on a corn plant. The moisture meter can use a spectrometry to determine the amount of moisture in the corn. A moisture meter includes a corn interface configured to conformingly engage the ear of corn when pressed against the ear of corn to form an optical seal about an opening through which the spectrometer determines the amount of moisture in the corn to inhibit ambient light from passing between the corn interface and the ear of corn into the at least one opening.

The present invention relates to the field of phenotyping, particularly to systems and methods for collecting, retrieval and processing of data for accurate and sensitive analysis and prediction of a phenotype of an object, particularly of a plant.

To mitigate degradation in the accuracy of a prediction model by re-learning the prediction model with consideration given to the characteristics of a detection value of a sensor.

This prediction model re-learning device comprises: a calculation unit that, on the basis of data related to smell detection by a sensor, calculates an index for determining whether or not to re-learn a prediction model for smell; and a re-learning unit that re-learns the prediction model in cases where the calculated index satisfies a predetermined condition.

A method for measuring performance of a geographic region from an image including a set of image elements includes: receiving the image corresponding to a time unit, generating a geographic region performance map for the image, combining the geographic region performance map with a second geographic region performance map, and generating a geographic region performance summary map. Generating the geographic region performance map for the image can include mapping a set of image elements to a set of geographic sub-regions, generating a set of vegetative performance values for the set of image elements, mapping the set of image elements to a set of crop types, defining a subset of image elements corresponding to a subset of vegetative performance values, comparing vegetative performance values of the subset of vegetative performance values, and generating geographic region performance values for the subset of image elements.

One embodiment relates to a system that includes a modular testing attachment comprising at least one of a crusher attachment, a squeezer attachment, and a blender attachment, the modular testing attachment configured to obtain a substance from food or crop products, a universal attachment coupled to the modular testing attachment and structured to receive and advance the substance from the food or crop products, a multispectral tester coupled to the universal attachment and structured to receive the substance from the universal attachment and test the substance to output scientific values, and a computing system operably coupled to the modular testing attachment and the multispectral tester and configured to selectively or automatically operate components thereof

Provided herein are chlorophyll polymers and conductive materials, sensors, and devices comprising the chlorophyll polymers, and methods of use and preparation thereof.

In some examples, a method is described. The method may include obtaining an electrical signal as an input signal. The method may also include generating a reference signal based on the input signal. Moreover, the method may include generating a device-under-test signal based on the input signal and based on a device-under-test capacitance between a first contact point and a second contact point. The first contact point may be electrically coupled to a first contact mechanism configured to contact a first contact area of the device-under-test. The second contact point may be configured to be electrically coupled to a second contact mechanism configured to contact a second contact area of the device-under-test. The method may also include generating a comparison signal based on a comparison between the reference signal and the device-under-test signal. Additionally, the method may include emitting an output signal that is based on the comparison signal.

In a measurement apparatus or a measurement method, an information processing apparatus executes: a process of specifying a bunch of grapes to be measured on the basis of detection of grasping of the bunch of grapes by a gripper; and a process of starting to measure the number of pieces of the specified bunch of grapes.

The present disclosure encompasses embodiments of X-ray computed tomography-based methods for the detection of onion quality factors. Such methods are advantageous in detecting internal damage to onion bulbs due to bacterial and fungal rots and mechanical damage while also providing for the overall assessment of onion bulb quality and market value. Because CT images provide cross-sectional reconstructions of the subject under study, CT scans of onion bulbs can be used not only to detect damage from disease, but also cuts and bruises that increase an onion bulb's susceptibility to disease, and the presence of shoots or seed stems and overall shape of the bulbs.

A method is provided for detecting a polypeptide of interest in a plant without the use of a reference standard. The method comprises the steps of obtaining a plant expressing the polypeptide of interest and a negative control plant that does not express the polypeptide of interest, and analyzing a sample from each in an information-dependent acquisition (IDA) method. A method is also provided for determining the relative expression level of a polypeptide of interest in a plurality of plants without the use of a reference standard. This method comprises the steps of obtaining a plurality of plants expressing the polypeptide of interest and a negative control plant that does not express the polypeptide of interest, analyzing samples from each in an IDA method, and determining the relative expression level of the polypeptide in each of the plurality of plants.