A sunlight exposure indicator device is disclosed that can determine the amount of time (e.g., hours) of PAR sunlight that occurs in a specific area for the optimal growth of a plant, as corresponds to the plant industry common designations of Full Shade, Partial Shade, Partial Sun and Full Sun. These designations can be used to determine plant selection for all types of plants including grasses, shrubs, flowers, vegetables and herbs, and trees. This device utilizes irreversible, slow-reacting, photochromic pigments applied to a substrate. Using multiple instances of this device will allow someone to easily test and accurately determine the amount of PAR sunlight (hours) received during a one-day sunlight cycle in multiple spots simultaneously. The sunlight exposure indicator device is a one-time-use, non-electronic, disposable device.

A sunlight exposure indicator device is disclosed that can determine the amount of time (e.g., hours) of PAR sunlight that occurs in a specific area for the optimal growth of a plant, as corresponds to the plant industry common designations of Full Shade, Partial Shade, Partial Sun and Full Sun. These designations can be used to determine plant selection for all types of plants including grasses, shrubs, flowers, vegetables and herbs, and trees. This device utilizes irreversible, slow-reacting, photochromic pigments applied to a substrate. Using multiple instances of this device will allow someone to easily test and accurately determine the amount of PAR sunlight (hours) received during a one-day sunlight cycle in multiple spots simultaneously. The sunlight exposure indicator device is a one-time-use, non-electronic, disposable device.

A method of cultivating a plant including a step of seeding a seed of the plant on a transparent medium and germinating and a step of evaluating and sorting the seed of the plant after a start of the germinating according to a germinating state, and a plant cultivation apparatus that can be used for the method of cultivating a plant.

The present disclosure relates to image processing or computer vision techniques. A computer-implemented method is provided for determining a damage status of a physical object, the method comprising the steps of receiving a surface image of the physical object; and providing a pre-trained machine learning model to derive property values from the received surface map, wherein each property value is indicative of a damage index at a respective location, wherein the property values are preferably usable for monitoring and/or controlling a production process of the physical object. In this way, it is possible to reliably identify local defects and ensure that it is accurate enough to apply the chemical products in suitable amounts.

The present disclosure relates to image processing or computer vision techniques. A computer-implemented method is provided for determining a damage status of a physical object, the method comprising the steps of receiving a surface image of the physical object; and providing a pre-trained machine learning model to derive property values from the received surface map, wherein each property value is indicative of a damage index at a respective location, wherein the property values are preferably usable for monitoring and/or controlling a production process of the physical object. In this way, it is possible to reliably identify local defects and ensure that it is accurate enough to apply the chemical products in suitable amounts.

An automated plant training system and related methods are designed to train medium to tall plants to grow in a height restricted space by adjusting the plant's direction of growth through the use of phototropism. The device can physically control the plant's main stem, branches and foliage from excessive vertical growth.

An automated plant training system and related methods are designed to train medium to tall plants to grow in a height restricted space by adjusting the plant's direction of growth through the use of phototropism. The device can physically control the plant's main stem, branches and foliage from excessive vertical growth.

Methods are provided for improving performance of a computing system used to model potential crop yield. In one example embodiment, a computer-implemented method includes generating a model of potential crop yield, as a function of planting date and relative maturity based, at least in part, on one or more relative maturity maps, one or more planting date maps, and one or more actual production history maps, and storing the model in a memory of the server computer system. The method also includes receiving, via an interface at a field manager computing device, a selection of a particular field and computing, from the model of potential crop yield, a potential yield for the particular field based, at least in part, on a planting date for the particular field, a relative maturity value, and values representing actual production history for the particular field.

Methods are provided for improving performance of a computing system used to model potential crop yield. In one example embodiment, a computer-implemented method includes generating a model of potential crop yield, as a function of planting date and relative maturity based, at least in part, on one or more relative maturity maps, one or more planting date maps, and one or more actual production history maps, and storing the model in a memory of the server computer system. The method also includes receiving, via an interface at a field manager computing device, a selection of a particular field and computing, from the model of potential crop yield, a potential yield for the particular field based, at least in part, on a planting date for the particular field, a relative maturity value, and values representing actual production history for the particular field.

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.