Few-Shot Learning approach for plant disease classification using images taken in the field
By David Argüeso, Artzai Picon, U. Irusta, Alfonso Medela, Miguel G. San-Emeterio, A. Bereciartua, Aitor Alvarez-Gila. Computers and Electronics in Agriculture, 2020
316 citations recorded by the source corpus.
Abstract Prompt plant disease detection is critical to prevent plagues and to mitigate their effects on crops. The most accurate automatic algorithms for plant disease identification using plant field images are based on deep learning. These methods require the acquisition and annotation of large image datasets, which is frequently technically or economically unfeasible. This study introduces Few-Shot Learning (FSL) algorithms for plant leaf classification using deep learning with small datasets. For the study 54,303 labeled images from the PlantVillage dataset were used, comprising 38 plant leaf and/or disease types (classes). The data was split into a source (32 classes) and a target (6 classes) domain. The Inception V3 network was fine-tuned in the source domain to learn general plant leaf characteristics. This knowledge was transferred to the target domain to learn new leaf types from few images. FSL using Siamese networks and Triplet loss was used and compared to classical fine-tuning transfer learning. The source and target domain sets were split into a training set (80%) to develop the methods and a test set (20%) to obtain the results. Algorithm performance was evaluated using the total accuracy, and the precision and recall per class. For the FSL experiments the algorithms were trained with different numbers of images per class and the experiments were repeated 20 times to statistically characterize the results. The accuracy in the source domain was 91.4% (32 classes), with a median precision/recall per class of 93.8%/92.6%. The accuracy in the target domain was 94.0% (6 classes) learning from all the training data, and the median accuracy (90% confidence interval) learning from 1 image per class was 55.5 (46.0–61.7)%. Median accuracies of 80.0 (76.4–86.5)% and 90.0 (86.1–94.2)% were reached for 15 and 80 images per class, yielding a reduction of 89.1% (80 images/class) in the training dataset with only a 4-point loss in accuracy. The FSL method outperformed the classical fine tuning transfer learning which had accuracies of 18.0 (16.0–24.0)% and 72.0 (68.0–77.3)% for 1 and 80 images per class, respectively. It is possible to learn new plant leaf and disease types with very small datasets using deep learning Siamese networks with Triplet loss, achieving almost a 90% reduction in training data needs and outperforming classical learning techniques for small training sets.
Subfields: Plant Science
View Paper: https://www.semanticscholar.org/paper/b6e6d06cda47ac32a375a4ec92264f9b27e526cf
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Plant Science
Citations in the corpus
- A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities (2022, methodology): “Up to now, the FSL and fine-tuning have been widely used in tasks like plant disease and insect pest identification [80], road detection [81], and automatic question and answer [82].”
- Plant Disease Detection and Classification by Deep Learning—A Review (2021, methodology): “[101] taken 38 kinds of plant disease images in the public dataset P1antVillage as the identification object, Siamese networks, and triplet loss was used and compared to classical fine-tuning transfer learning.”
- Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification (2021, methodology): “[85] introduced FSL algorithms for plant disease classification to address the problem of requiring large annotative image datasets for DL methods.”