Abstract
Detection of fruits for automatic harvesting using vision sensors has gained attention in recent times. In this work, we consider the problem of the detection of ripe cherry bunches for the selective harvesting of coffee cherries. The coffee cherries are small in size and appear in a clustered arrangement which makes it difficult to detect them. Also, the previous studies indicate that the accuracy of the available techniques for fruit detection is not sufficient for use in real-time harvesting operations. Hence, we propose a novel Hybrid Consensus and Recovery Block (HCRB) based technique for the reliable detection of the ripe coffee cherry bunches for the coffee harvester robot using RGB-D sensor. Our studies via simulation as well as on hardware set-up indicate a significant increase in true positive and decrease in false positive detections which makes it suitable for use in real-time harvesters. The proposed system provides accuracy, precision, recall, and F1- score of 93%, 97%, 92%, and 93% respectively when tested on the NVIDIA Jetson Nano Development board.
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The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 6
Claim · model-written summary
True positive detections significantly increased.
CoffeaNot reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“a significant increase in true positive and decrease in false positive detections”
t0“Coffee”
Claim · model-written summary
False positive detections significantly decreased.
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“a significant increase in true positive and decrease in false positive detections”
t0“Coffee”
Claim · model-written summary
Accuracy was 93%.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“accuracy, precision, recall, and F1- score of 93%, 97%, 92%, and 93% respectively when tested on the NVIDIA Jetson Nano Development board”
t0“Coffee”
Claim · model-written summary
Precision was 97%.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“accuracy, precision, recall, and F1- score of 93%, 97%, 92%, and 93% respectively when tested on the NVIDIA Jetson Nano Development board”
t0“Coffee”
Claim · model-written summary
Recall was 92%.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“accuracy, precision, recall, and F1- score of 93%, 97%, 92%, and 93% respectively when tested on the NVIDIA Jetson Nano Development board”
t0“Coffee”
Claim · model-written summary
F1 score was 93%.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“accuracy, precision, recall, and F1- score of 93%, 97%, 92%, and 93% respectively when tested on the NVIDIA Jetson Nano Development board”
t0“Coffee”