Agricultural pest dataset

The Agricultural Pest Dataset is a large-scale benchmark dataset designed specifically for pest identification tasks. The dataset contains 102 different categories of pests, covering common pest species in agriculture, forestry, and household environments. About 100 high-quality images are selected for each pest, and then the images are flipped, rotated, zoomed, cropped, grayscale, exposed, blurred, and noisy to expand the dataset. The final dataset consists of 36771 images, with each image size reset to 640 * 640. All images are annotated by professional technicians and comply with the YOLO11 model specifications. Among them, there were 33870 participants in the training group, 1939 participants in the validation group, and 962 participants in the testing group. 102 pest names:'rice leaf roller ','rice leaf caterpillar ','paddy stem maggot ','asiatic rice borer ','yellow rice borer ','rice gall midge ','Rice Stemfly ','brown plant hopper ','white backed plant hopper ','small brown plant hopper ','rice water weevil ','rice leafhopper ','grain spreader thrips ','rice shell pest ','grub ','mole cricket ','wireworm ','white margined moth ','black cutworm ','large cutworm ','yellow cutworm ','red spider ','corn borer ','army worm ','aphids ','Potosiabre vitarsis ','peach borer ','english grain aphid ','green bug ','bird cherry-oataphid ',' wheat blossom midge ','penthaleus major ','longlegged spider mite ','wheat phloeothrips ','wheat sawfly','cerodonta denticornis','beet fly','flea beetle','cabbage army worm','beet army worm','Beet spot flies','meadow moth','beet weevil','sericaorient alismots chulsky ','alfalfa weevil','flax budworm','alfalfa plant bug','tarnished plant bug ','Locustoidea','lytta polita','legume blister beetle ','blister beetle','therioaphis maculata Buckton ','odontothrips loti','Thrips','alfalfa seed chalcid ','Pieris canidia','Apolygus lucorum','Limacodidae','Viteus vitifoliae','Colomerus vitis','Brevipoalpus lewisi McGregor ','oides decempunctata','Polyphagotars onemus latus','Pseudococcus comstocki Kuwana ','parathrene regalis','Ampelophaga','Lycorma delicatula','Xylotrechus','Cicadella viridis','Miridae','Trialeurodes vaporariorum','Erythroneura apicalis','Papilio xuthus','Panonchus citri McGregor','Phyllocoptes oleiverus ashmead','Icerya purchasi Maskell','Unaspis yanonensis','Ceroplastes rubens','Chrysomphalus aonidum','Parlatoria zizyphus Lucus','Nipaecoccus vastalor','Aleurocanthus spiniferus','Tetradacus c Bactrocera minax','Dacus dorsalis(Hendel)','Bactrocera tsuneonis','Prodenia litura','Adristyrannus','Phyllocnistis citrella Stainton','Toxoptera citricidus','Toxoptera aurantii','Aphis citricola Vander Goot','Scirtothrips dorsalis Hood','Dasineura sp','Lawana imitata Melichar','Salurnis marginella Guerr','Deporaus marginatus Pascoe','Chlumetia transversa','Mango flat beak leafhopper','Rhytidodera bowrinii white','Sternochetus frigidus','Cicadellidae' The main application scenarios of this dataset are as follows:  Pest classification: used for training and evaluating pest classification models to help identify different types of pests.  Object detection: used to train and evaluate pest object detection models, helping to locate pests in images.  Agricultural intelligence: Develop an intelligent pest monitoring and management system by combining other agricultural data.