Abstract
India has a large population of spinach eaters. Despite this fact most people and young generation have difficulty in distinguishing the spinach species because of the structure similarity of many plant species. So, automated spinach recognition will support the people community to a greater extent. In this study, we present spinach dataset, a freely accessible annotated collection of images of spinach leaves in Indian scenario. We propose three different custom designed convolutional neural networks (CNN) and compare the performance of the same. Also we apply the transfer learning approach using MobileNetV2 pretrained model for this spinach species recognition. Using transfer learning approach we got an accuracy of 92.96%.
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This study is done as a part of Kaggleá¾½s Open Data Research Grant 2020.
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Ahila Priyadharshini, R., Arivazhagan, S., Arun, M. (2023). A Curated Dataset for Spinach Species Identification. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_17
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