Affiliation:
1. Indian Council of Agricultural Research
2. Indian Institute of Technology Kharagpur
Abstract
Abstract
Robotic harvesting of cotton bolls will incorporate the
benefits of manual picking as well as mechanical harvesting. For
robotic harvesting, in-field cotton segmentation with minimal
errors is desirable which is a challenging task. In the present
study, three lightweight fully convolutional neural network models
were developed for the semantic segmentation of in-field cotton
bolls. Model 1 does not include any residual or skip connections,
while model 2 consists of residual connections to tackle the
vanishing gradient problem and skip connections for feature
concatenation. Model 3 along with residual and skip connections,
consists of filters of multiple sizes. The effects of filter size
and the dropout rate were studied. All proposed models segment the
cotton bolls successfully with the cotton-IoU
(intersection-over-union) value of above 88%. The highest
cotton-IoU of 91.03% was achieved by model 2. The proposed models
achieved F1-score and pixel accuracy values greater than 95% and
98%, respectively. The developed models were compared with existing
state-of-the-art networks namely VGG19, ResNet18, EfficientNet-B1,
and InceptionV3. Despite having a limited number of trainable
parameters, the proposed models achieved mean-IoU (mean
intersection-over-union) of 93.84%, 94.15%, and 94.65% against the
mean-IoU values of 95.39%, 96.54%, 96.40%, and 96.37% obtained
using state-of-the-art networks. The segmentation time for the
developed models was reduced up to 52% compared to state-of-the-art
networks. The developed lightweight models segmented the in-field
cotton bolls comparatively faster and with greater accuracy. Hence,
developed models can be deployed to cotton harvesting robots for
real-time recognition of in-field cotton bolls for harvesting.
Publisher
Research Square Platform LLC