ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

MangoNet: A deep semantic segmentation architecture for a method to detect and count mangoes in an open orchard

Kestur, Ramesh and Meduri, Avadesh and Narasipura, Omkar (2019) MangoNet: A deep semantic segmentation architecture for a method to detect and count mangoes in an open orchard. In: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 77 . pp. 59-69.

[img] PDF
Eng_App_Art_Int_77_59_2019.pdf - Published Version
Restricted to Registered users only

Download (4MB) | Request a copy
Official URL: https://doi.org/10.1016/j.engappai.2018.09.011

Abstract

This work presents a method for detection and counting of mangoes in RGB images for further yield estimation. The RGB images are acquired in open field conditions from a mango orchard in the pre-harvest stage. The proposed method uses MangoNet, a deep convolutional neural network based architecture for mango detection using semantic segmentation. Further, mango objects are detected in the semantic segmented output using contour based connected object detection. The MangoNet is trained using 11,096 image patches of size 200 x 200 obtained from 40 images. Testing was carried out on 1500 image patches generated from 4 test images. The results are analyzed for performance of segmentation and detection of mangoes. Results are analyzed using the precision, recall, Fl parameters derived from contingency matrix. Results demonstrate the robustness of detection for a multitude of factors such as scale, occlusion, distance and illumination conditions, characteristic to open field conditions. The performance of the MangoNet is compared with FCN variant architectures trained on the same data. MangoNet outperforms its variant architectures.

Item Type: Journal Article
Additional Information: Copyright of this article belongs to PERGAMON-ELSEVIER SCIENCE LTD
Keywords: Deep learning; Semantic segmentation; Object detection; Mango detection; Convolutional neural networks; Fully convolutional networks
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Depositing User: Francis Jayakanth
Date Deposited: 10 Feb 2019 08:55
Last Modified: 10 Feb 2019 08:55
URI: http://eprints.iisc.ac.in/id/eprint/61338

Actions (login required)

View Item View Item