A temperature control system comprising a first peripheral fluid circuit for the passage of a first heat exchanger fluid. The first peripheral fluid circuit comprises a first fluid connection for fluidly connecting a first peripheral heat exchanger in series with a first peripheral-evaporator heat exchanger. There is also provided a first peripheral pump for pumping the first heat exchanger fluid around the first peripheral fluid circuit. There is also provided a first evaporator circuit for the passage of an evaporator heat exchanger fluid through the first peripheral-evaporator heat exchanger. The first evaporator circuit comprises a first evaporator pump for pumping the evaporator heat exchanger fluid around the first evaporator circuit. The first evaporator circuit is fluidly isolated from the first peripheral fluid circuit. The first peripheral-evaporator heat exchanger is configured to permit heat exchange between the heat exchanger fluids.

A system for identifying ponding water located on a field from image data is described. In an approach, an image of an agricultural field is analyzed using a classifier that has been trained based on the spectral bands of labeled image pixels to identify a probability for each pixel within the image that the pixel corresponds to water. A flow simulation is performed to determine regions of the field that are likely to pool water after rainfall based on precipitation data, elevation data, and soil property data of the field. A graph of vertices representing the pixels and edges representing connections between neighboring pixels is generated. The probability of each pixel within the graph being ponding water is set based on the probability pixel being water, the likelihood that water will pool in the area represented by the pixel, the probability of neighboring pixels being ponding water, and a cropland mask that identifies which pixels correspond to cropland. A class for each pixel is then determined that maximizes the joint probability over the graph.

The present disclosure provides for an electronic sensor for detecting a root of a plant in soil, the electronic sensor that includes a first conductor plate configured to be disposed in soil, a switch, a power supply, and signal extractor. The switch is electrically coupled to the first conductor plate and is configured to switch between a first mode and a second mode. The power supply is electrically coupled to the switch and is configured to provide an electrical charge to the first conductor plate in the first mode of the switch. The signal extractor is electrically coupled to the switch and is configured to extract a signal response at the first conductor plate in the second mode of the switch. The present disclosure further provides a second conductor plate configured to be disposed in soil adjacent to and substantially parallel to the first conductor plate. The second conductor plate is electrically coupled to ground.

A method of detecting a potato virus in a crop image depicting at least one potato plant includes storing the crop image in a memory, identifying a first region of the crop image depicting potato plant leaves, identifying a plurality of edges within the first region, determining whether an image segment of the crop image within the first region satisfies one or more leaf creasing criteria symptomatic of leaf creasing caused by the virus based on the edges that are located within the image segment, determining whether the image segment satisfies one or more color criteria symptomatic of discoloration caused by the virus, and determining whether the segment displays symptoms of potato virus based on whether the image segment satisfies one or more of the leaf creasing criteria and the color criteria. A system and computer readable medium are also disclosed.

A method of detecting a potato virus in a crop image depicting at least one potato plant includes storing the crop image in a memory, identifying a first region of the crop image depicting potato plant leaves, identifying a plurality of edges within the first region, determining whether an image segment of the crop image within the first region satisfies one or more leaf creasing criteria symptomatic of leaf creasing caused by the virus based on the edges that are located within the image segment, determining whether the image segment satisfies one or more color criteria symptomatic of discoloration caused by the virus, and determining whether the segment displays symptoms of potato virus based on whether the image segment satisfies one or more of the leaf creasing criteria and the color criteria. A system and computer readable medium are also disclosed.

An image capture system for a grow pod includes a master controller that has a processor, a memory, and cameras that are communicatively coupled to the master controller and positioned to capture images of plants or seeds. The memory stores a grow recipe and a logic. The grow recipe defines instructions for growing the plants or seeds and expected attributes corresponding to the instructions. The logic, when executed by the processor, causes the master controller to perform at least the following: receive, from the cameras, the images of the plants or seeds, determine attributes of the plants or seeds from the images, compare the attributes of the plants or seeds from the images to the expected attributes defined by the grow recipe, and adjust the instructions of the grow recipe for growing the plants or seeds based on the comparison of the attributes to the expected attributes.

An image capture system for a grow pod includes a master controller that has a processor, a memory, and cameras that are communicatively coupled to the master controller and positioned to capture images of plants or seeds. The memory stores a grow recipe and a logic. The grow recipe defines instructions for growing the plants or seeds and expected attributes corresponding to the instructions. The logic, when executed by the processor, causes the master controller to perform at least the following: receive, from the cameras, the images of the plants or seeds, determine attributes of the plants or seeds from the images, compare the attributes of the plants or seeds from the images to the expected attributes defined by the grow recipe, and adjust the instructions of the grow recipe for growing the plants or seeds based on the comparison of the attributes to the expected attributes.

To provide a vascular sap measurement sensor in which a flow channel that receives incoming flow of vascular sap is unlikely to be blocked by tissues of a plant. A vascular sap measurement sensor 1 includes: a trapping probe 20 for trapping vascular sap; and a support 10 that supports the trapping probe 20. A trapping flow channel 21 that receives incoming flow of the vascular sap is formed in the trapping probe 20. The trapping flow channel 21 has an inlet opening 24 formed on a side surface of the trapping probe 20. This makes it unlikely that the trapping flow channel 21 will be blocked by tissues of a plant when sticking the trapping probe 20 into the plant.

To provide a vascular sap measurement sensor in which a flow channel that receives incoming flow of vascular sap is unlikely to be blocked by tissues of a plant. A vascular sap measurement sensor 1 includes: a trapping probe 20 for trapping vascular sap; and a support 10 that supports the trapping probe 20. A trapping flow channel 21 that receives incoming flow of the vascular sap is formed in the trapping probe 20. The trapping flow channel 21 has an inlet opening 24 formed on a side surface of the trapping probe 20. This makes it unlikely that the trapping flow channel 21 will be blocked by tissues of a plant when sticking the trapping probe 20 into the plant.

A signal processing apparatus, method, and program that enable growth conditions of vegetation to be easily confirmed are disclosed. An NDVI relative value calculation provides a relative value to an average of a vegetation index indicative of growth conditions of grass, on the basis of a sensing image. An image indicative of the growth conditions of an inspection object can be displayed on the basis of the relative value. Further, an NDVI average calculation section calculates an NDVI average obtained by averaging normalized difference vegetation indexes NDVI indicative of the growth conditions of the grass in the entire grass, and a correlation coefficient calculation section calculates a correlation coefficient that matches the NDVI average with a predetermined NDVI specified value. The NDVI relative value calculation applies the correlation coefficient in providing the relative value in each measurement unit in which a measurement is performed on the grass.