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首页> 外文会议>Canadian Conference on Artificial Intelligence >In Vino Veritas: Estimating Vineyard Grape Yield from Images Using Deep Learning
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In Vino Veritas: Estimating Vineyard Grape Yield from Images Using Deep Learning

机译：在Vino Veritas中：使用深度学习从图像估计葡萄园的葡萄产量

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Agricultural harvest estimation is an important, yet challenging problem to which machine learning can be applied. There is value in having better methods of yield estimation based on data that can be captured with inexpensive technology in the field. This research investigates five approaches to using convolution neural networks (CNNs) to develop models that can estimate the weight of grapes on the vine from an image taken by a smartphone. The results indicate that a combination of image processing and deep CNN machine learning can produce models that are sufficiently accurate within a variety of grape for data captured at harvest time. The best approach involved transfer learning; where a CNN is developed starting from the weights of a pretrained density map model that learns to output the location of grapes in the image. The best model achieved a MAE of 157 g over a mean average weight of 1335 g, or a MAE% of 11.8.
机译：农业收成估算是一个重要但具有挑战性的问题，可以应用机器学习。有一种基于可以用本领域廉价技术捕获的数据的更好的产量估计方法的价值。这项研究调查了五种使用卷积神经网络（CNN）来开发模型的方法，这些模型可以根据智能手机拍摄的图像估算葡萄在葡萄树上的重量。结果表明，将图像处理与深度CNN机器学习相结合，可以生成在各种葡萄中对于收获时捕获的数据足够准确的模型。最好的方法是转学。从预先训练的密度图模型的权重开始开发CNN，该模型学会在图像中输出葡萄的位置。最好的模型在平均平均重量1335 g上实现了157 g的MAE，或11.8的MAE％。

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