Jesús Dassaef López-Barrios

Mr. Jesús Dassaef López-Barrios

Tecnologico de Monterrey, Querétaro, Mexico
orcid

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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21 May 2023 in Applied Sciences
Verified
Journal Article
Open access
PDF file
JLJesús Dassaef López-Barrios
Jesús Dassaef López-Barrios
Jesús Arturo Escobedo CabelloJesús Arturo Escobedo Cabello
Jesús Arturo Escobedo Cabello
Alfonso Gómez-EspinosaAlfonso Gómez-Espinosa
Alfonso Gómez-Espinosa
LMLuis-Enrique Montoya-Cavero
Luis-Enrique Montoya-Cavero
In this paper, a mask region-based convolutional neural network (Mask R-CNN) is used to improve the performance of machine vision in the challenging task of detecting peduncles and fruits of green sweet peppers (Capsicum annuum L.) in greenhouses. One of the most complicated stages of the sweet pepper harvesting process is to achieve a precise cut of the peduncle or stem because this type of specialty crop cannot be grabbed and pulled by the fruit since the integrity and value of the product are compromised. Therefore, accurate peduncle detection becomes vital for the autonomous harvesting of sweet peppers. ResNet-101 combined with the feature pyramid network (FPN) architecture (ResNet-101 + FPN) is adopted as the backbone network for feature extraction and object representation enhancement at multiple scales. Mask images of fruits and peduncles are generated, focused on green sweet pepper, which is the most complex color variety due to its resemblance to the background. In addition to bounding boxes, Mask R-CNN provides binary masks as a result of instance segmentation, which would help improve the localization process in 3D space, the next phase of the autonomous harvesting process of sweet peppers, since it isolates the pixels belonging to the object and demarcates its boundaries. The prediction results of 1148 fruits on 100 test images showed a precision rate of 84.53%. The prediction results of 265 peduncles showed a precision rate of 71.78%. The mean average precision rate with an intersection over union at 50 percent (mAP@IoU=50) for model-wide instance segmentation was 72.64%. The average detection time for sweet pepper fruit and peduncle using high-resolution images was 1.18 s. The experimental results show that the proposed implementation manages to segment the peduncle and fruit of the green sweet pepper in real-time in an unmodified production environment under occlusion, overlap, and light variation conditions with effectiveness not previously reported for simultaneous 2D detection models of peduncles and fruits of green sweet pepper.

17 October 2024
Verified
Book Chapter
Closed access
PDF file
JLJesús Dassaef López-Barrios
Jesús Dassaef López-Barrios
IAIlse Karena de Anda-García
Ilse Karena de Anda-García
RJRaul Jimenez-Cruz
Raul Jimenez-Cruz
Luis A. TrejoLuis A. Trejo
Luis A. Trejo
GRGilberto Ruiz
Gilberto Ruiz
Miguel Gonzalez-MendozaMiguel Gonzalez-Mendoza
Miguel Gonzalez-Mendoza

14 June 2026
Verified
Book Chapter
Closed access
NNNezih Nieto
Nezih Nieto
JLJesús Dassaef López-Barrios
Jesús Dassaef López-Barrios
BTBenjamin Telles-Ramírez
Benjamin Telles-Ramírez
JPJuan Vladimir Padilla-López
Juan Vladimir Padilla-López
IAIlse Karena De Anda-García
Ilse Karena De Anda-García
Hector G. CeballosHector G. Ceballos
Hector G. Ceballos