A deep learning approach for detection and localization of leaf anomalies
This work addresses crop disease monitoring for agricultural applications, but it is incremental as it compares existing unsupervised methods on a specific dataset.
The paper tackled the problem of detecting and localizing leaf diseases in crops by applying unsupervised autoencoders to a dataset of pepper and cherry leaf images, finding that the vector-quantized variational autoencoder performed best across all evaluation metrics.
The detection and localization of possible diseases in crops are usually automated by resorting to supervised deep learning approaches. In this work, we tackle these goals with unsupervised models, by applying three different types of autoencoders to a specific open-source dataset of healthy and unhealthy pepper and cherry leaf images. CAE, CVAE and VQ-VAE autoencoders are deployed to screen unlabeled images of such a dataset, and compared in terms of image reconstruction, anomaly removal, detection and localization. The vector-quantized variational architecture turns out to be the best performing one with respect to all these targets.