A vegetation wall system includes an irrigation tank, a vegetation mounting unit with a vegetation holding unit, a pump feeding water from the tank to the holding unit, a programmable logic control unit including a central processing unit and a sensor, wherein the central processing unit monitors an aspect in the wall system by measuring, by the sensor, a component of the aspect as a first component measurement, waiting a predetermined period of time and then measuring, by the sensor, the component of the aspect as a second component measurement, determining, by the central processing unit, a change in the aspect based on the first and second measurements and the predetermined period of time, determining whether the change is outside a threshold range, in response to determining that the change is outside the threshold range, determining that an anomalous condition exists, and generating an alert when the anomalous condition exists.

A yield calculation system comprises a position sensor configured to detect a position. A baler comprises a bale chamber in which crop material is to be formed into a bale, a volume measurement sensor provided in the bale chamber and configured to measure a volume of the bale in the bale chamber, the volume corresponding to the position detected by the position sensor, and a moisture measurement sensor provided in the bale chamber and configured to measure a moisture amount in the bale, the moisture amount corresponding to the position detected by the position sensor. Circuitry is configured to calculate, based on the volume of the bale and the moisture amount corresponding to the position, a yield corresponding to the position by excluding the moisture amount from an amount of the bale.

An information processing apparatus comprises a first obtaining unit configured to obtain a predicted value of a yield of crops in a section where the crops are cultivated, a second obtaining unit configured to obtain a yield of the crops harvested in the section, and a control unit configured to notify progress of harvesting of the crops in the section, which is determined based on the predicted value obtained by the first obtaining unit and the yield obtained by the second obtaining unit.

The present embodiments generally relate to methods of non-destructively imaging plant root damage by insect root herbivores and evaluating the efficacy of insecticidal materials associated with the roots of plants against the insect root herbivores, useful for automated high throughput bioassays.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

The present disclosure relates to methods and systems for obtaining image information of an organism including a set of optical data; calculating a growth index based on the set of optical data; and calculating an anticipated harvest time based on the growth index, where the image information includes at least one of: (a) visible image data obtained from an image sensor and non-visible image data obtained from the image sensor, and (b) a set of image data from at least two image capture devices, where the at least two image capture devices capture the set of image data from at least two positions.

A method of harvesting plant product from a plant in a single pass using a combine harvester is disclosed. In the method, the plant has a protein content gradient that varies along a height of the plant. The method includes identifying, along a longitudinally-extending stalk of the plant, an upper protein gradient of the plant including high protein plant product and a lower protein gradient of the plant including lower protein plant product, wherein the high protein plant product from the upper protein gradient of the plant meets a threshold protein content that is higher than that of the lower protein plant product. The method also includes separately and substantially simultaneously harvesting the high protein plant product from the upper protein gradient and the lower protein plant product from the lower protein gradient in the single pass, and isolating the high protein plant product from the lower protein plant product.

A sensing system contains a vibration device attached to a stem of an agricultural crop for applying vibration to the agricultural crop, at least one sensor attached to the stem of the agricultural crop for sensing vibration of the agricultural crop caused by the vibration applied to the agricultural product from the vibration device to transmit vibration information relate to the vibration of the agricultural crop and a computing device for identifying one local maximum value among a plurality of local maximum values in a frequency spectrum obtained from the vibration information received from the at least one sensor as a resonance frequency of the vibration of the agricultural crop to determine a growth degree of a fruit of the agricultural crop based on the identified resonance frequency.

Examples of the disclosure describe systems and methods for identifying, quantifying, and/or characterizing plant stomata. In an example method, a first set of two or more images of a plant leaf representing two or more focal distances is captured via an optical sensor. A reference focal distance is determined based on the first set of images. A second set of two or more images of the plant leaf is captured via the optical sensor, including at least one image captured at a focal distance less than the reference focal distance, and at least one image captured at a focal distance greater than the reference focal distance. A composite image is generated based on the second set of images. The composite image is provided to a trainable feature detector in order to determine a number, density, and/or distribution of stomata in the composite image.

A prediction server includes: a data save unit that acquires diagnosis data from a diagnosis server; a training unit that constructs a prediction model that predicts a possibility of occurrence of the disorder in an arbitrary area and on an arbitrary date by training a learning model about a correlation among an area indicated by first position information, a date indicated by a diagnosis date, and a type of disorder in a machine learning manner, in which the type of the disorder is a diagnosis result estimated by the diagnosis server based on a diseased portion image by using a diagnosis model trained about a correlation between the diseased portion image and the disorder in the machine learning manner.