02179naa a2200385 a 450000100080000000500110000800800410001902200140006002400460007410000220012024501600014226000090030252010340031165000190134565000220136465000200138665000140140665000190142065000110143965000190145065000200146965000170148965000270150665000090153365300200154265300150156265300190157765300250159665300110162170000150163270000260164770000200167370000180169377300820171121448772022-08-22       2022    bl uuuu     u00u1 u    #d  a0168-16997 adoi.org/10.1016/j.compag.2022.1071322DOI1 aPEREIRA, R. de C.  aDetection and classification of whiteflies and development stages on soybean leaves images using an improved deep learning strategy.h[electronic resource]  c2022  aThis paper presents a novel strategy to detect and classify adult whiteflies and five important related stages on images of detached soybean leaves. The whitefly Bemisia tabaci is a major pest in soybean crops, and by detecting, counting and differentiating its related life stages in field collected leaves control management decisions can be made. The proposed solution is based on a deep learning object detection algorithm (YOLOv4), modified into an specific new learning strategy with innovations on data augmentation, image mosaicking, and fusion of hypothesized object categories. A real and annotated dataset of images is provided from a controlled experiment infected with whitefly eggs having 121 images and 973 annotated objects. The experimental results showed a promising performance of the proposed system, reaching an f1-score of 0.87, in comparison with a single YOLOv4 algorithm that reached f1-score of 0.80. The overall strategy could be extended to work in other similar tasks for image based pest management.  aClassification  aComputer software  aComputer vision  aDetection  aDigital images  aModels  aBemisia Tabaci  aIdentificação  aMosca Branca  aPrograma de Computador  aSoja  aClassificação  aDetecção  aImagem digital  aVisão computacional  aYolov41 aHIROSE, E.1 aCARVALHO, O. L. F. de1 aCOSTA, R. M. da1 aBORGES, D. L.  tComputers and Electronics in Agriculturegv. 199, 107132, p. 1-12, Aug. 2022.