A server of a crop growth stage determination system includes a processor. The processor inputs first images obtained by image capturing crops in a manner such that crop shapes are extractable. The Processor inputs growth stages each indicating a level of physiological growth of the crops for each of the first images. The processor constructs a learned model by performing deep learning to associate images of the crops and growth stages of the crops based on the input first images and the input growth stage. The processor inputs a second image obtained by image capturing crops a growth stage of which is unknown, in a manner such that crop-shapes are extractable. The processor determines the growth stage for the input second image based on the learned model. The processor outputs the determined growth stage.

A vascular sap measurement sensor includes an indicator electrode probe, a reference electrode probe, and a supporting portion. The indicator electrode probe is an ion-sensitive field effect transistor. The reference electrode probe includes a solid reference electrode, the solid reference electrode including a base layer, a silver chloride layer, and a chloride layer, the base layer being formed of an electrically conductive body, the silver chloride layer being formed on a surface of the base layer, the chloride layer being formed on a surface of the silver chloride layer. The supporting portion supports the indicator electrode probe and the reference electrode probe arranged in parallel.

A computer-implemented method for determining soil and/or vegetation properties of an agricultural field, wherein the method includes the steps of: receiving remote data over a plurality of time frames, wherein the remote data includes data from at least one determined location including a plurality of spectral bands or optical domains of different wavelengths; and processing the remote data, wherein processing the remote data includes the steps of: generating at least one coefficient derived from the remote data; determining a rate of change of the at least one coefficient at the plurality of time frames; and determining at least one soil and/or vegetation property value based on the rate of change of the at least one generated coefficient for at least one of the plurality of time frames.

A mobile platform includes at least one first sensor mounted at a first position on the mobile platform and at least one second sensor mounted at a second position on the mobile platform, where the first position is offset from the second position. The at least one first sensor is configured to capture first data measurements of plants in the growing area. The at least one second sensor is configured to capture second data measurements of the plants in the growing area. Each of the first and second data measurements is associated with a three-dimensional position within the growing area and a time. The first and second sensors are configured to generate at least one common type of data measurement such that at least some of the first and second data measurements represent stereo-spatio-temporal data measurements of the plants in the growing area.

A mobile platform includes at least one first sensor mounted at a first position on the mobile platform and at least one second sensor mounted at a second position on the mobile platform, where the first position is offset from the second position. The at least one first sensor is configured to capture first data measurements of plants in the growing area. The at least one second sensor is configured to capture second data measurements of the plants in the growing area. Each of the first and second data measurements is associated with a three-dimensional position within the growing area and a time. The first and second sensors are configured to generate at least one common type of data measurement such that at least some of the first and second data measurements represent stereo-spatio-temporal data measurements of the plants in the growing area.

An inspection system is presented for use in monitoring plants' conditions in a plant growing area. The inspection system comprises: an optical probe comprising at least one imaging set, each imaging set comprising: a flash illuminator unit; an imaging unit configured with a predetermined resolution; and a sensing unit; the optical probe being configured and operable to perform one or more imaging sessions on a target in a plant growing area at a target location during a movement of the optical probe along a movement path in a vicinity of the target location, said sensing unit comprising a distance sensing element configured and operable to determine an instantaneous distance between the imaging unit and the target being imaged, and generate distance sensing data indicative thereof; and a control unit configured and operable to be responsive to the distance sensing data to initiate the imaging session and synchronize operation of the flash illuminator unit and the imaging unit to capture images of the target by the optical probe, thereby enabling analyzing the images and determining a condition of the target being indicative of at least one of pest, insect and disease presence at the target.

An inspection system is presented for use in monitoring plants' conditions in a plant growing area. The inspection system comprises: an optical probe comprising at least one imaging set, each imaging set comprising: a flash illuminator unit; an imaging unit configured with a predetermined resolution; and a sensing unit; the optical probe being configured and operable to perform one or more imaging sessions on a target in a plant growing area at a target location during a movement of the optical probe along a movement path in a vicinity of the target location, said sensing unit comprising a distance sensing element configured and operable to determine an instantaneous distance between the imaging unit and the target being imaged, and generate distance sensing data indicative thereof; and a control unit configured and operable to be responsive to the distance sensing data to initiate the imaging session and synchronize operation of the flash illuminator unit and the imaging unit to capture images of the target by the optical probe, thereby enabling analyzing the images and determining a condition of the target being indicative of at least one of pest, insect and disease presence at the target.

The amount of carbon dioxide absorbed by plants grown in a field can more accurately be estimated based on environmental information of the field. A plurality of types of mutually-differing environmental information are measured regarding an installation field. For the plurality of types of environmental information, the correspondence relationship between each type of environmental information and a carbon dioxide (CO.sub.2) absorption amount is stored. A plurality of CO.sub.2 absorption amounts are acquired by referring to the respective correspondence relationship for each type of measured environmental information. The acquired plurality of CO.sub.2 absorption amounts are compared with each other and the minimum value thereof is selected as the estimated value. Information regarding the selected estimated value is displayed.

Disclosed herein is a system and methods for leaf detection and extraction. The extracted leaf may be used for leaf water potential analysis. In some embodiments, the method comprises identifying the leaf from a point cloud based on an image, determining a pose of the leaf based on the point cloud, and cutting and storing the leaf based on the pose of the leaf.

The present invention makes an efficient diagnosis of an object. A general server 3 of a diagnostic assistance system 1 provides data (a large volume of photographic images) concerning an object managed by each client, to said client and a plurality of analysts and experts, and allows such diagnosers to diagnose the object and enables sharing of diagnosis results among the diagnosers. The plurality of analysts each partially contribute in viewing the large volume of photographic images so as to find an abnormal site in the object. The client and the experts then conduct a more detailed diagnosis on the abnormal site discovered by the analysts. An AI server 5 of the diagnostic assistance system 1 creates training data from the diagnosis results provided by the plurality of diagnosers, performs machine learning on the diagnosis results, and carries out automated diagnosis using the learned method.