Abstract: A fruit picking method based on a three-dimensional parameter prediction model for a fruit comprises: performing first-time image acquisition processing on a to-be-picked fruit to obtain a first image; determining a first range; controlling a manipulator to perform first-time moving processing; performing intermittent gas injection treatment to lead to forced vibration of the to-be-picked fruit; performing second-time image acquisition processing many times to obtain a plurality of second images; screening out, by taking the first image as an reference object, two appointed second images deviating from an equilibrium position to the maximum extent; jointly inputting the images into a preset three-dimensional parameter prediction model for the fruit so as to obtain predicted three-dimensional parameters; controlling the manipulator to perform second-time moving processing; and performing cutting processing on a fruit stem position to make the to-be-picked fruit fall onto the manipulator.

Abstract: Disclosed is a fruit picking method based on a visual servo control robot, comprising: placing a throwing apparatus and a fixed photosensitive device at a first position to obtain a fixed photosensitive image; generating a first throwing path, a second throwing path, and a third throwing path; arranging recovery apparatuses; performing simultaneous rotational throwing processing to throw a first wireless photosensitive device, a second wireless photosensitive device, and a third wireless photosensitive device; receiving a first photosensitive image sequence and a second photosensitive image sequence of the first wireless photosensitive device and the second wireless photosensitive device; receiving a third photosensitive image sequence of the third wireless photosensitive device; generating a spatial position of a fruit on a to-be-picked fruit tree; and performing fruit picking processing using the visual servo control robot according to the spatial position of the fruit.

Abstract: Disclosed is a fruit picking method based on a visual servo control robot, comprising: placing a throwing apparatus and a fixed photosensitive device at a first position to obtain a fixed photosensitive image; generating a first throwing path, a second throwing path, and a third throwing path; arranging recovery apparatuses; performing simultaneous rotational throwing processing to throw a first wireless photosensitive device, a second wireless photosensitive device, and a third wireless photosensitive device; receiving a first photosensitive image sequence and a second photosensitive image sequence of the first wireless photosensitive device and the second wireless photosensitive device; receiving a third photosensitive image sequence of the third wireless photosensitive device; generating a spatial position of a fruit on a to-be-picked fruit tree; and performing fruit picking processing using the visual servo control robot according to the spatial position of the fruit.

Abstract: Disclosed is a fruit picking method based on a visual servo control robot, comprising: placing a throwing apparatus and a fixed photosensitive device at a first position to obtain a fixed photosensitive image; generating a first throwing path, a second throwing path, and a third throwing path; arranging recovery apparatuses; performing simultaneous rotational throwing processing to throw a first wireless photosensitive device, a second wireless photosensitive device, and a third wireless photosensitive device; receiving a first photosensitive image sequence and a second photosensitive image sequence of the first wireless photosensitive device and the second wireless photosensitive device; receiving a third photosensitive image sequence of the third wireless photosensitive device; generating a spatial position of a fruit on a to-be-picked fruit tree; and performing fruit picking processing using the visual servo control robot according to the spatial position of the fruit.

Abstract: Disclosed is a method for detecting an infection stage of anthracnose pathogen with pre-analysis capacity, comprising: obtaining a plurality of sample sensing data sequences; obtaining a sample citrus leaf image; obtaining a first prediction result; if the first prediction result is that the sample citrus crop is not infected by anthracnose, obtaining sample Raman spectral data and sample hyperspectral data; obtaining a first judgment result, and obtaining a second judgment result; performing labeling to obtain second training data; training a neural network model to obtain a second anthracnose prediction model; obtaining a plurality of to-be-analyzed sensing data sequences; obtaining a to-be-analyzed citrus leaf image; obtaining a second prediction result; if the second prediction result is that the to-be-analyzed citrus crop is not infected by anthracnose, obtaining a third prediction result.

Abstract: A citrus identification method comprises: performing first-time image acquisition processing on a target citrus tree to obtain a first image; inputting the first image into a first citrus fruit identification model to be processed to obtain a first identification result sequence; performing area interception processing on the first image to obtain a citrus fruit area; obtaining roundness integrity numerical values; selecting an appointed roundness integrity numerical value, and acquiring a defect position of an appointed citrus fruit in the first image; determining a first spatial range and a second spatial range; performing both first spray injection treatment and second spray injection treatment; performing second-time image acquisition processing to obtain a second image; inputting the second image into a second citrus fruit identification model to be processed to obtain a second identification result; and generating a citrus fruit identification result.

Abstract: The application discloses a fruit picking method based on a three-dimensional parameter prediction model for a fruit.

Abstract: A citrus identification method comprises: performing first-time image acquisition processing on a target citrus tree to obtain a first image; inputting the first image into a first citrus fruit identification model to be processed to obtain a first identification result sequence; performing area interception processing on the first image to obtain a citrus fruit area; obtaining roundness integrity numerical values; selecting an appointed roundness integrity numerical value, and acquiring a defect position of an appointed citrus fruit in the first image; determining a first spatial range and a second spatial range; performing both first spray injection treatment and second spray injection treatment; performing second-time image acquisition processing to obtain a second image; inputting the second image into a second citrus fruit identification model to be processed to obtain a second identification result; and generating a citrus fruit identification result.

Abstract: Disclosed is a method for detecting an infection stage of anthracnose pathogen with pre-analysis capacity, comprising: obtaining a plurality of sample sensing data sequences; obtaining a sample citrus leaf image; obtaining a first prediction result; if the first prediction result is that the sample citrus crop is not infected by anthracnose, obtaining sample Raman spectral data and sample hyperspectral data; obtaining a first judgment result, and obtaining a second judgment result; performing labeling to obtain second training data; training a neural network model to obtain a second anthracnose prediction model; obtaining a plurality of to-be-analyzed sensing data sequences; obtaining a to-be-analyzed citrus leaf image; obtaining a second prediction result; if the second prediction result is that the to-be-analyzed citrus crop is not infected by anthracnose, obtaining a third prediction result.