Abstract
Computer vision for crop sciences is overgrowing with the advancement in pattern recognition and deep learning. Agriculture pattern segmentation is an important application such as segmentation of cloud shadow, waterway, standing water, weed cluster, planter skip, double plant, etc. However, the segmentation of agriculture patterns is challenging due to multi-scale variations of patterns and considerable overlap between classes. Furthermore, size and shape variation, unclear boundaries, and missing edges make this task more complex. To address this problem, we proposed the encoder-decoder architecture with input as a multi-scale image pyramid to improve the multi-scale feature extraction of agriculture patterns. EfficientNetB7 is utilized as an encoder for efficient feature extraction. In addition, the proposed Lateral-output layer shows rich semantic features by aggregating low level and high level features from the decoder, shows improvement in dice score. The proposed research uses the Agriculture Vision dataset with aerial farmland images. The proposed method outperformed different state-of-the-art methods in the literature for six agricultural patterns segmentation with a mean dice score of 74.78, 68.11, and 84.23 for RGB, NIR, and RGB \(+\) NIR images, respectively.
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Bhagat, S., Kokare, M., Haswani, V., Hambarde, P., Kamble, R. (2022). MS-Net: A CNN Architecture for Agriculture Pattern Segmentation in Aerial Images. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-031-11346-8_42
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