Abstract
AbstractDeep learning models have been successfully deployed for a diverse array of image‐based plant phenotyping applications including disease detection and classification. However, successful deployment of supervised deep learning models requires large amount of labeled data, which is a significant challenge in plant sciences (and most biological) domain due to the inherent complexities. Specifically, data annotation is costly, laborious, time consuming and needs domain expertise for phenotyping tasks, especially for diseases. To overcome this challenge, active learning algorithms have been proposed to reduce the amount of labeling needed by deep learning models to achieve good predictive performance. Active learning methods work by adaptively suggesting samples to annotate using an acquisition function to achieve maximum (classification) performance under a fixed labeling budget. We report the performance of four different active learning methods, (1) Deep Bayesian Active Learning (DBAL), (2) Entropy, (3) Least Confidence, and (4) core‐set, with conventional random sampling‐based annotation for two vastly different image‐based classification datasets. The first image dataset consists of soybean [Glycine max L. (Merr.)] leaves belonging to eight different soybean stresses and a healthy class, and the second consists of nine different weed species from the field. For a fixed labeling budget, we observed that the classification performance of deep learning models using active learning based acquisition strategies is better than random sampling‐based acquisition for both datasets. The integration of active learning strategies for data annotation can help mitigate labelling challenges in the plant sciences applications particularly where resources dedicated to annotations are limited.
Highlights
Introduction and Related WorkDeep learning architectures have advanced the state-of-the-art performance for image-based classification tasks [21], and have been successfully deployed for a diverse array of image-based plant phenotyping applications applications including disease detection, classification and quantification [27,34]
For the soybean stress classification dataset, we clearly observe that all the uncertainty sampling based active learning methods outperform random sampling whereas diversity sampling based Coreset method underperforms
The performance gain due to Active Learning (AL) methods over random sampling for plant domain datasets is similar to the improvement observed in other domain datasets like MNIST and CIFAR10 [5]
Summary
Introduction and Related WorkDeep learning architectures have advanced the state-of-the-art performance for image-based classification tasks [21], and have been successfully deployed for a diverse array of image-based plant phenotyping applications applications including disease detection, classification and quantification [27,34]. One of the critical drawbacks of deep learning models is its necessity to have a large amount of labeled data to achieve good model accuracy This is especially true for plant science applications, where annotating data can be costly, laborious, and time consuming to obtain, and generally need domain expertise (for instance, for plant stress image labeling that requires trained plant pathologists). To overcome this drawback, one effective and practical strategy is to use Active Learning (AL) based image annotation [9]. The goal of AL is to achieve maximum predictive performance under a fixed labeling budget, which makes it desirable for plant science applications
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
