Data augmentation for automated pest classification in Mango farms

Kusrini, Kusrini; Suputa, Suputa; Setyanto, Arief; Agastya, I Made Artha; Priantoro, Herlambang; Chandramouli, Krishna; Izquierdo, Ebroul

doi:10.1016/j.compag.2020.105842

Cited by 76 publications

(34 citation statements)

References 14 publications

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“…The proposed method augments the data required for learning and solves the problem of the lack of data through the image quality-evaluation process. Not only in the medical field but also in various areas, such as defect inspection in a smart factory, pest classification, and distracted driving detection, the problem of data shortage is being solved by data augmentation [21][22][23]. The method proposed herein is expected to be applicable to not only medical images but also various areas where data are insufficient.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Computer-Aided Diagnosis System for Gastroscopy Image Classification Using Synthetic Data

Kim

Cho

2021

Applied Sciences

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Gastric cancer has a high mortality rate worldwide, but it can be prevented with early detection through regular gastroscopy. Herein, we propose a deep learning-based computer-aided diagnosis (CADx) system applying data augmentation to help doctors classify gastroscopy images as normal or abnormal. To improve the performance of deep learning, a large amount of training data are required. However, the collection of medical data, owing to their nature, is highly expensive and time consuming. Therefore, data were generated through deep convolutional generative adversarial networks (DCGAN), and 25 augmentation policies optimized for the CIFAR-10 dataset were implemented through AutoAugment to augment the data. Accordingly, a gastroscopy image was augmented, only high-quality images were selected through an image quality-measurement method, and gastroscopy images were classified as normal or abnormal through the Xception network. We compared the performances of the original training dataset, which did not improve, the dataset generated through the DCGAN, the dataset augmented through the augmentation policies of CIFAR-10, and the dataset combining the two methods. The dataset combining the two methods delivered the best performance in terms of accuracy (0.851) and achieved an improvement of 0.06 over the original training dataset. We confirmed that augmenting data through the DCGAN and CIFAR-10 augmentation policies is most suitable for the classification model for normal and abnormal gastric endoscopy images. The proposed method not only solves the medical-data problem but also improves the accuracy of gastric disease diagnosis.

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Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Computer-Aided Diagnosis System for Gastroscopy Image Classification Using Synthetic Data

Kim

Cho

2021

Applied Sciences

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“…This dataset has 3662 images and consists of 1805 images diagnosed as nondiabetic (labeled as 0) retinopathy and 1857 images diagnosed as diabetic retinopathy, as shown in Figure 5. The pest classi f ication in mango f arms dataset [37] is a collection of 46,500 images of mango leaves affected by 15 different types of pests and one normal (unaffected) mango leaf, as shown in Figure 7. Some of these pests can be detected visually.…”

Section: Image Datasetsmentioning

confidence: 99%

Cubical Homology-Based Machine Learning: An Application in Image Classification

Choe

Ramanna

2022

Axioms

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Persistent homology is a powerful tool in topological data analysis (TDA) to compute, study, and encode efficiently multi-scale topological features and is being increasingly used in digital image classification. The topological features represent a number of connected components, cycles, and voids that describe the shape of data. Persistent homology extracts the birth and death of these topological features through a filtration process. The lifespan of these features can be represented using persistent diagrams (topological signatures). Cubical homology is a more efficient method for extracting topological features from a 2D image and uses a collection of cubes to compute the homology, which fits the digital image structure of grids. In this research, we propose a cubical homology-based algorithm for extracting topological features from 2D images to generate their topological signatures. Additionally, we propose a novel score measure, which measures the significance of each of the sub-simplices in terms of persistence. In addition, gray-level co-occurrence matrix (GLCM) and contrast limited adapting histogram equalization (CLAHE) are used as supplementary methods for extracting features. Supervised machine learning models are trained on selected image datasets to study the efficacy of the extracted topological features. Among the eight tested models with six published image datasets of varying pixel sizes, classes, and distributions, our experiments demonstrate that cubical homology-based machine learning with the deep residual network (ResNet 1D) and Light Gradient Boosting Machine (lightGBM) shows promise with the extracted topological features.

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“…This benchmark dataset is a valuable resource for the development of crop monitoring and management approaches, allowing researchers to test model performance over a wide range of pests. This dataset can also be complemented with the mango ( Mangifera indica ) pest classification dataset, which has images of mango plants infected with 15 different categories of pests, with a large volume of augmented images to increase model robustness ( Kusrini et al, 2020a ). Precise algorithms for the detection of pests can support assessing crop resistance by counting the pests, helping identify pest species in the field, and monitoring pest spread.…”

Section: Applications Of Htpmentioning

confidence: 99%

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

et al. 2021

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High throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors such as RGB cameras, hyperspectral sensors and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterise crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalising between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation and quantitative trait measurement. We emphasise the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.

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Data augmentation for automated pest classification in Mango farms

Cited by 76 publications

References 14 publications

Deep Learning-Based Computer-Aided Diagnosis System for Gastroscopy Image Classification Using Synthetic Data

Deep Learning-Based Computer-Aided Diagnosis System for Gastroscopy Image Classification Using Synthetic Data

Cubical Homology-Based Machine Learning: An Application in Image Classification

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

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