A regular monitoring of plants is inevitable to ensure an effective production and to reduce yield losses, for example, caused by different diseases. Infected plants show a visual effect shortly after inoculation. These effects can be understood as anomalies, which do not occur in healthy plant stocks. For automation of harvesting or spraying it is important to recognize anomalies to ensure an on-time reaction by the farmer or breeder. However, these anomalies differ largely in their appearance and a representative model is generally too complex to be learned. Our main objective is reconstruction-based anomaly detection by all-convolutional autoencoder (all-CAE), which combines convolutions with the architecture of an autoencoder (AE). To achieve our objective, we use an hourglass all-convolutional encoder-decoder architecture to create a highly compressed representation in the middle layer. Moreover, we compare different types of noise as regularizer. In our experiments, the method is tested on images of grapes acquired in a vineyard. We show that all-CAE are suitable for anomaly detection and that unnatural noise (salt) shows the best results.
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Detection of Anomalous Grapevine Berries Using All-Convolutional Autoencoders
Semantic Scholar · Computer Science · 2019
Abstract
A regular monitoring of plants is inevitable to ensure an effective production and to reduce yield losses, for example, caused by different diseases. Infected plants show a visual effect shortly after inoculation. These effects can be understood as anomalies, which do not occur in healthy plant stocks. For automation of harvesting or spraying it is important to recognize anomalies to ensure an on-time reaction by the farmer or breeder. However, these anomalies differ largely in their appearance and a representative model is generally too complex to be learned. Our main objective is reconstruction-based anomaly detection by all-convolutional autoencoder (all-CAE), which combines convolutions with the architecture of an autoencoder (AE). To achieve our objective, we use an hourglass all-convolutional encoder-decoder architecture to create a highly compressed representation in the middle layer. Moreover, we compare different types of noise as regularizer. In our experiments, the method is tested on images of grapes acquired in a vineyard. We show that all-CAE are suitable for anomaly detection and that unnatural noise (salt) shows the best results.