4 results about "GREEN BELL PEPPER" patented technology

This invention belongs to the field of pesticide residue detection technology, specifically relating to a method for detecting flufenoxuron and its active metabolites in plant-derived foods. The detection method includes: mixing and shaking the plant-derived food to be tested with a formic acid-acetonitrile solution; mixing and shaking the resulting mixture with sodium chloride and magnesium sulfate, followed by solid-liquid separation to obtain an extract; purifying the extract by mixing it with 10-30 nm multi-walled carbon nanotubes, followed by solid-liquid separation to obtain a test solution; and performing liquid chromatography-mass spectrometry (LC-MS) on the test solution to determine the content of flufenoxuron and its active metabolites in the plant-derived food based on the obtained peak area and a predetermined standard curve. The detection method provided by this invention can simultaneously detect flufenoxuron and its active metabolites with high sensitivity. In the examples, two compounds showed good repeatability and reproducibility in five matrices, enabling simultaneous detection of flufenoxuron and its active metabolites in spinach, green peppers, lemons, sweet oranges, and tea.

1. The name of the design product: bottle sticker (fresh green pepper sauce). 2. The use of the design product: for bottle stickers. 3. The design points of the design product: the combination of shape and pattern. 4. The picture or photo that best indicates the design points: front view. 5. The design product is a flat product, omitting the left view; the design product is a flat product, omitting the right view; the design product is a flat product, omitting the top view; the design product is a flat product, omitting the bottom view.

The present application relates to a kind of intelligent green pepper picking machine, including walking mechanism, support frame, mechanical joint arm, intelligent identification module, shearing assembly and controller, support frame is rotatably connected on walking mechanism, one end of mechanical joint arm is connected on support frame, shearing assembly is connected at the other end of mechanical joint arm, for picking mature green pepper, intelligent identification module is set on support frame, for identifying green pepper and obtaining mature green pepper position coordinates, controller is electrically connected with intelligent identification module, mechanical joint arm, shearing assembly and walking mechanism, and for according to mature green pepper position coordinates control mechanical joint arm, shearing assembly and walking mechanism collaborative action shearing mature green pepper.The present application is automatically identified and positioned green pepper by intelligent identification module, then by controller cooperates walking mechanism, mechanical joint arm and shearing assembly automatically completes green pepper picking, reduces manual labor intensity.

The application discloses a YOLOv5l green pepper intelligent detection method based on NSGA-II pruning, which comprises the following steps: collecting field green pepper image data; marking green pepper fruits in the image by using LabelIMG; preparing a green pepper dataset and dividing the dataset into a training set and a test set; obtaining a green pepper detection model by adopting a pruning YOLOv5l model method based on NSGA-II; detecting the reliability and accuracy of the pruning YOLOv5l green pepper detection model of NSGA-II in the field, and evaluating the model; if the reliability and accuracy meet the set requirements, the next step is carried out; if the reliability and accuracy do not meet the set requirements, the above steps are re-performed, and the number of green pepper images is increased and the network is modified; and the model is deployed to a green pepper phenotype monitoring and green pepper robot picking production application scene, so that the parameter quantity, model size and GFlops of the green pepper detection model are greatly reduced, and the detection speed is significantly improved.