The current invention relates to an artificial intelligence, internet of things (IoT) based water body management system. The water body management system of the invention comprises of an aeration module, a nutrient dispensing module, and a sensor module which monitors and maintains water quality parameters. The artificial intelligence module configured with the sensor module trains the data obtained from the sensors and inputs to the aeration, nutrient dispensing or other modules. The current invention has applications in aquaculture management, management of water bodies like lakes, reservoirs, and ponds.

The current invention relates to an artificial intelligence, internet of things (IoT) based water body management system. The water body management system of the invention comprises of an aeration module, a nutrient dispensing module, and a sensor module which monitors and maintains water quality parameters. The artificial intelligence module configured with the sensor module trains the data obtained from the sensors and inputs to the aeration, nutrient dispensing or other modules. The current invention has applications in aquaculture management, management of water bodies like lakes, reservoirs, and ponds.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that validate the synchronization of controllers in an aquaculture environment. One of the methods includes an image processor that receives images generated by a first image generating device that includes a light filter that is associated with light of a particular light frequency while an aquaculture environment was illuminated with light. Based on the image that was generated by the first image generating device, the image processor determines whether the intensity value of the light frequency in the image satisfies a threshold value. Based on determining whether the intensity value of the light frequency in the image satisfies the threshold value, the image processor determines whether the aquaculture environment was illuminated with light of the particular light frequency when the image was generated. The image processor provides for output an indication of whether the aquaculture was illuminated with light of the particular frequency when the image was generated.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that validate the synchronization of controllers in an aquaculture environment. One of the methods includes an image processor that receives images generated by a first image generating device that includes a light filter that is associated with light of a particular light frequency while an aquaculture environment was illuminated with light. Based on the image that was generated by the first image generating device, the image processor determines whether the intensity value of the light frequency in the image satisfies a threshold value. Based on determining whether the intensity value of the light frequency in the image satisfies the threshold value, the image processor determines whether the aquaculture environment was illuminated with light of the particular light frequency when the image was generated. The image processor provides for output an indication of whether the aquaculture was illuminated with light of the particular frequency when the image was generated.

Provided are compositions, methods, and solutions for generating aqueous glucomannan solutions with hydrogen compositions greater than 100 parts per billion. Said glucomannan solutions have application in nutritional, therapeutic, and energy fields.

Provided are compositions, methods, and solutions for generating aqueous glucomannan solutions with hydrogen compositions greater than 100 parts per billion. Said glucomannan solutions have application in nutritional, therapeutic, and energy fields.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for visually detecting a halocline. In some implementations, a method includes moving a camera through different depths of water within a fish enclosure, capturing, at the different depths, images of fish, determining that changes in focus in the images correspond to changes in depth that the images were captured, and based on determining that the changes in focus in the images correspond to the changes in depths that the images were captured, detecting a halocline at a particular depth.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for visually detecting a halocline. In some implementations, a method includes moving a camera through different depths of water within a fish enclosure, capturing, at the different depths, images of fish, determining that changes in focus in the images correspond to changes in depth that the images were captured, and based on determining that the changes in focus in the images correspond to the changes in depths that the images were captured, detecting a halocline at a particular depth.

The present disclosure provides a method for artificial rearing of porcupine pufferfish fries in indoor cement ponds. The method includes the following steps: nursery pond preparation, ponding of newly hatched prelarvae, feeding strategies at fry rearing stages, water quality management during fry rearing, separate ponding of fries, and emergence of fries. Compared with the prior art, the method realizes large-scale production of artificial rearing of porcupine pufferfish fries through a strict control of the water quality and environmental conditions of a nursery pond, along with scientific and reasonable feeding and operation management based on biological characteristics and nutrient requirements of the porcupine pufferfish at different fry breeding stages.

The present disclosure provides a method for artificial rearing of porcupine pufferfish fries in indoor cement ponds. The method includes the following steps: nursery pond preparation, ponding of newly hatched prelarvae, feeding strategies at fry rearing stages, water quality management during fry rearing, separate ponding of fries, and emergence of fries. Compared with the prior art, the method realizes large-scale production of artificial rearing of porcupine pufferfish fries through a strict control of the water quality and environmental conditions of a nursery pond, along with scientific and reasonable feeding and operation management based on biological characteristics and nutrient requirements of the porcupine pufferfish at different fry breeding stages.