Our AI models are trained to recognize the species listed to the right. More common species with more data tend to be easier for our program to recognize while species with less samples are harder to identify. As more specimens are identified using our app, our dataset grows and our models improve their accuracy on the most common species uploaded.
•Acanthoscelides griseolus
•Acanthoscelides obtectus
•Acanthoscelides pallidipennis
•Acanthoscelides zeteki
•Algarobius prosopis
•Bruchidius uberatus
•Bruchus affinis
•Bruchus ermaginatus
•Bruchus lentis
•Bruchus rufimanus
•Bruchus signaticornis
•Callosobruchus chinensis
•Callosobruchus phaseoli
•Callosobruchus rhodesianus
•Caryedon gonagra
•Decellebruchus atrolineatus
•Mimosestes amicus
•Mimosestes nubigens
•Pseudopachymerina spinipes
•Stator limbatus
•Stator vachelliae
•Acanthoscelides argillaceus
•Acanthoscelides macrophthalmus
•Acanthoscelides obvelatus
•Acanthoscelides quadridentatus
•Algarobius bottimeri
•Bruchidius badjii
•Bruchidius villosus
•Bruchus brachialis
•Bruchus ervi
•Bruchus pisorum
•Bruchus rufipes
•Callosobruchus analis
•Callosobruchus maculatus
•Callosobruchus pulcher
•Callosobruchus theobromae
•Caryobruchus gleditsiae
•Megabruchidius tonkineus
•Mimosestes mimosae
•Pachymerus nucleorum
•Specularius impressithorax
•Stator pruininus
•Zabrotes subfasciatus
Our program focuses on quick, easy, and reliable Bruchinae classification. Our AI models provide their best results when the provided images are consistent with the images used in training. The tool tips on our upload page provide examples and explanations of how to create a consistent image for our program to evaluate. Cropping the image is not necessary as long as the beetle appears in a similar manner to the image to the left.
Aligning the beetle properly under the microscope and ensuring that the specimen is clearly separated from the microscope's vignette helps the model by preventing excess dark spots in the cropped image. Key features should be in focus in the image such as the elytra for the dorsal view and the head for the frontal view. If unsure about what to focus the microscope on, try to get as much of the beetle in focus as possible for the best results. The image on the right shows an improperly aligned specimen which is very close to the vignette and mostly out of focus.
Exposing the specimen properly helps with improving the classification results. Under and overexposed beetles may cause their coloration to be harder to distinguish. Our image preprocessing helps reduce differences caused by exposure levels and enhances the colors provided. A well exposed image always provide better results.
Our preprocessing utilizes a simple object detection model to crop images so that the beetle fills the whole image. We then use color and feature enhancing techniques before feeding the images to their respective models. We use Resnet50 models from pyTorch and our database of over 10,000 images to create strong image recognition models trained purely for recognizing Bruchinae species. The program will save your previous 50 classifications in your history. Our admin can review and verify your classifications which will then be added to our training data to improve our model.
