Abstract
Agricultural yield estimation from natural images is a challenging problem to which machine learning can be applied. Convolutional Neural Networks have advanced the state of the art in many machine learning applications such as computer vision, speech recognition and natural language processing. The proposed research uses convolution neural networks to develop models that can estimate the weight of grapes on a vine using an image. Trained and tested with a dataset of 60 images of grape vines, the system manages to achieve a cross-validation yield estimation accuracy of 87%.
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Notes
- 1.
This research is a collaboration with Lightfoot and Wolfville Vineyard of Wolfville, Nova Scotia, Canada.
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Acknowledgement
I would like to pay special thankfulness to my supervisor Dr. Daniel Silver for his vital support and assistance.
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Monga, T. (2018). Estimating Vineyard Grape Yield from Images. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_37
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DOI: https://doi.org/10.1007/978-3-319-89656-4_37
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