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公开(公告)号：US20230176022A1
公开(公告)日：2023-06-08
申请号：US17911629
申请日：2021-03-12
发明人： Satoshi ITO
IPC分类号： G01N33/00
CPC分类号： G01N33/007 , G01N33/0006
摘要： The present invention relates to an information processor. The information processer comprises a diagnostic unit for diagnosing a state of a humidity sensor, the diagnostic unit diagnosing that the humidity sensor is in a deteriorated state when days on which a proportion of detection data of the humidity sensor indicating 100% humidity out of all detection data for one day is equal to or greater than a reference proportion, have persisted for a reference period.
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公开(公告)号：US20220279731A1
公开(公告)日：2022-09-08
申请号：US17619194
申请日：2020-05-29
发明人： Satoshi ITO
摘要： An information processing device comprising an information acquisition unit for acquiring a measurement information of a relative humidity inside a plastic greenhouse. The information processing device further comprising a prediction unit for generating a feature value representing a dryness condition inside the plastic greenhouse using the measurement information of the relative humidity. The prediction unit is further configured for predicting a risk of diseases and pests inside the plastic greenhouse on the basis of the feature value.
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公开(公告)号：US20230115408A1
公开(公告)日：2023-04-13
申请号：US17911632
申请日：2021-03-12
发明人： Satoshi ITO , Asako MORI , Takahiro SASSA
摘要： Problem To provide an information processor capable of effectively reducing a risk of pest damage to produce. Solution An information processor 10 predicts a risk of pest damage to produce, the information processor comprising: a prediction unit for predicting an effect of reducing the pest damage risk for each of a plurality of countermeasure candidates for changing at least one influencing parameter affecting the pest damage risk; and a selection unit for selecting a countermeasure from among the plurality of countermeasure candidates while prioritizing a countermeasure having a higher effect of reducing the pest damage risk, on the basis of a result of predicting the effect of reducing the pest damage risk afforded by the prediction unit.
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公开(公告)号：US20220357481A1
公开(公告)日：2022-11-10
申请号：US17620647
申请日：2020-05-29
发明人： Satoshi ITO , Victoria SMART , Mehul BANSAL
摘要： An information processing device is configured to acquire prediction information of a weather condition outside a plastic greenhouse and cultivation information of produce inside the plastic greenhouse and predict an environmental condition inside the plastic greenhouse based on the prediction information of the weather condition and the cultivation information. Cultivation information includes at least one item of information from among the type of produce, a cultivation amount, a growth state, and a cultivation ground. A prediction model may be generated with machine learning.
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公开(公告)号：US20220076014A1
公开(公告)日：2022-03-10
申请号：US17417494
申请日：2019-10-10
发明人： Satoshi ITO
摘要： The present disclosure makes it possible to more accurately estimate information relating to crop infection, on the basis of more appropriate cultivation information. Provided is an information processing device comprising: a feature quantity conversion unit that generates a feature quantity by performing a feature quantity conversion of at least one of piece of information among information pieces relating to three factors causing disease in crops, namely a primary cause, a predisposition and an inducement; and an estimation unit that estimates crop infection information on the basis of an infection estimation model which is used to estimate infection information relating to infection of the crop, which is generated on the basis of a machine-learning algorithm, and which uses as input the information relating to the three factors, including the feature quantity.
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公开(公告)号：US20230301246A1
公开(公告)日：2023-09-28
申请号：US18023311
申请日：2021-08-20
发明人： Satoshi ITO
摘要： [Summary] [Problem] To improve accuracy in relation to predicting the future environment inside a greenhouse. [Solution] An information processing device (100) is provided with: an extra-atmospheric solar irradiance calculating unit (140) for calculating the extra-atmospheric solar irradiance in each time span on each date at an installation location of a greenhouse (3A, 3B) in which a crop is cultivated; a machine learning unit (131) for performing machine learning of a greenhouse environment prediction model on the basis of actual result information relating to the environment inside the greenhouse (3A, 3B), using the extra-atmospheric solar irradiance and meteorological predicted information as inputs; a predicting unit (132) for predicting the environment in the greenhouse (3A, 3B) using the greenhouse environment prediction model; and an output control unit (133) for controlling the output of predicted information relating to the predicted environment inside the greenhouse (3A, 3B).
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