Plant leaf deep semantic segmentation and a novel benchmark dataset for morning glory plant harvesting

Computer vision and deep learning have made substantial progress in the areas of agriculture and smart
farming, particularly for enhancing crop production using image segmentation techniques for crop yield
prediction. Further improvements to crop yield prediction results can be achieved by developing accurate
and efficient methods. In response to such demands, this paper proposes a novel convolutional neural
network architecture, called densely connected SegNet (D-SegNet) and demonstrates its advantages on plant segmentation using a new morning glory plant dataset, and also on a complimentary publicly available dataset to promote research in this direction. The D-SegNet is evaluated using 10-fold cross validation. It achieves performance better than the state-of-the-art SegNet algorithm. The evaluated precision, recall and F1-score values are 98.20%, 90.64% and 94.26%, respectively, for the morning glory plant dataset. The intersection over union (IoU) value in the image segmentation tasks is 90.56%. A series of experiments on the morning glory plant dataset as well as on the publicly available dataset were conducted. The results show that the proposed method achieves accurate segmentation results and can be useful for assessing the plant weight during harvesting. In summary, this new plant segmentation network, D-SegNet, could form an important component of future cloud-based machine learning systems to predict crop yield from noisy smartphone images taken in the field.