mlsanityData to 14 Oct 2026

A deep learning approach for detection and localization of leaf anomalies

arXiv 2210.03558 ↗First posted 7 Oct 2022cs.CV cs.LG stat.MLcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 37 references that are in the map. First posted on arXiv 7 Oct 2022.

Builds on

10 of 37 references.

References · 10

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2017Neural Discrete Representation Learningcited by 4,712
  4. 2019An Introduction to Variational Autoencoderscited by 1,000
  5. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
  6. 2016Using Deep Learning for Image-Based Plant Disease Detectioncited by 219
  7. 2020Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Datacited by 42
  8. 2021Unsupervised Brain Anomaly Detection and Segmentation with Transformerscited by 34
  9. 2021Stride and Translation Invariance in CNNscited by 7
  10. 2020Improving unsupervised anomaly localization by applying multi-scale memories to autoencoderscited by 5

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026AgriKD: Cross-Architecture Knowledge Distillation for Efficient Leaf Disease Classification
  2. 2025Plant Disease Detection through Multimodal Large Language Models and Convolutional Neural Networks
  3. 2026Cross-Dataset Generalization of Bangladeshi Rice Leaf Disease Classifiers: Benchmark, Diagnosis, and Mitigation
  4. 2025StateSpace-SSL: Linear-Time Self-supervised Learning for Plant Disease Detection
  5. 2025A Vision-Language Foundation Model for Leaf Disease Identification
  6. 2025Exploring Model Quantization in GenAI-based Image Inpainting and Detection of Arable Plants
  7. 2026TinyCNN: A 193K-Parameter Network for On-Device Plant Disease Detection, with a Cross-Dataset Robustness Diagnosis
  8. 2022Device-friendly Guava fruit and leaf disease detection using deep learning