Authors:
Soofiyan Atar
1
;
Simranjeet Singh
1
;
Jaison Jose
2
and
Kavi Arya
1
Affiliations:
1
Indian Institute of Technology Bombay, Mumbai, India
;
2
St. Vincent Pallotti College of Engineering and Technology, Nagpur, India
Keyword(s):
Robotic Harvesting, Semantic Map, Instance Segmentation, Image Classification.
Abstract:
Harvesting tomatoes in agriculture is a time-consuming and repetitive task. Different techniques such as accurate detection, classification, and exact location of tomatoes must be utilized to automate harvesting tasks. This paper proposes a perception pipeline (P2Ag) that can effectively harvest tomatoes using instance segmentation, classification, and semantic mapping techniques. P2Ag is highly optimized for embedded hardware in terms of performance, computational power and cost. It provides decision-making approaches for harvesting along with perception techniques, using a semantic map of the environment. This research offers an end- to-end perception solution for autonomous agricultural harvesting. To evaluate our approach, we designed a simulator environment with tomato plants and a stereo-vision sensor. This paper reports results on detecting tomatoes (actual and simulated ) and marking each tomato’s location in 3D space. In addition, the evaluation shows that the proposed P2Ag
outperforms the state-of-the-art implementations.
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