Recognition of diseased Pinus trees in UAV images using deep learning and AdaBoost classifier

Hu, Gensheng; Yin, Cunjun; Wan, Mingzhu; Zhang, Yan; Fang, Yi

doi:10.1016/j.biosystemseng.2020.03.021

Cited by 88 publications

(42 citation statements)

References 21 publications

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“…The model reached an overall accuracy of 85%. In another study [78], the authors attempted to detect disease in pinus trees using UAV images. The classification model was designed based on a combination of deep convolutional generative adversarial networks (DCGANs), and an AdaBoost classifier.…”

Section: Uav Imagingmentioning

confidence: 99%

Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research

et al. 2021

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Crop diseases constitute a serious issue in agriculture, affecting both quality and quantity of agriculture production. Disease control has been a research object in many scientific and technologic domains. Technological advances in sensors, data storage, computing resources and artificial intelligence have shown enormous potential to control diseases effectively. A growing body of literature recognizes the importance of using data from different types of sensors and machine learning approaches to build models for detection, prediction, analysis, assessment, etc. However, the increasing number and diversity of research studies requires a literature review for further developments and contributions in this area. This paper reviews state-of-the-art machine learning methods that use different data sources, applied to plant disease detection. It lists traditional and deep learning methods associated with the main data acquisition modalities, namely IoT, ground imaging, unmanned aerial vehicle imaging and satellite imaging. In addition, this study examines the role of data fusion for ongoing research in the context of disease detection. It highlights the advantage of intelligent data fusion techniques, from heterogeneous data sources, to improve plant health status prediction and presents the main challenges facing this field. The study concludes with a discussion of several current issues and research trends.

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Section: Uav Imagingmentioning

confidence: 99%

Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research

et al. 2021

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“…It implements a metaalgorithm that can be used in conjunction with many other learning algorithms to improve their performance. In our study, we have considered 1 as a subset for training [54,64,65]. The advantages and disadvantages of these methods are compared in Table 4.…”

Section: And Decision Tree (Dt) Classifies Instances or Examples Intomentioning

confidence: 99%

The prediction of mortality influential variables in an intensive care unit: a case study

Khajehali

Tarokh

2021

Pers Ubiquit Comput

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The intensive care units (ICUs) are among the most expensive and essential parts of all hospitals for extremely ill patients. This study aims to predict mortality and explore the crucial factors affecting it. Generally, in the health care systems, having a fast and precise ICU mortality prediction for patients plays a key role in care quality, resulting in reduced costs and improved survival chances of the patients. In this study, we used a medical dataset, including patients' demographic details, underlying diseases, laboratory disorder, and LOS. Since accurate estimates are required to have optimal results, various data pre-processings as the initial steps are used here. Besides, machine learning models are employed to predict the risk of mortality ICU discharge. For AdaBoost model, these measures are considered AUC= 0.966, sensitivity (recall) = 87.88%, Kappa=0.859, F-measure = 89.23% making it, AdaBoost, accounts for the highest rate. Our model outperforms other comparison models by using various scenarios of data processing. The obtained results demonstrate that the high mortality can be caused by underlying diseases such as diabetes mellitus and high blood pressure, moderate Pulmonary Embolism Wells Score risk, platelet blood count less than 100000 (mcl), hypertension (HTN), high level of Bilirubin, smoking, and GCS level between 6 and 9.

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“…The weight of each weak regression is calculated according to the predicting accuracy of each sample in each training set and the overall predicting accuracy of the last training set. In addition, the distribution weight of each sample is updated and the regression results obtained from each training are weighted and summed as the final output result of the strong regression [42,43]. The specific processes of the AdaBoost algorithm are as follows [44,45]:…”

Section: Adaboost Modelmentioning

confidence: 99%

A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

et al. 2021

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Selecting internal hyperparameters, which can be set by the automatic search algorithm, is important to improve the generalization performance of machine learning models. In this study, the geological, remote sensing and geochemical data of the Lalingzaohuo area in Qinghai province were researched. A multi-source metallogenic information spatial data set was constructed by calculating the Youden index for selecting potential evidence layers. The model for mapping mineral prospectivity of the study area was established by combining two swarm intelligence optimization algorithms, namely the bat algorithm (BA) and the firefly algorithm (FA), with different machine learning models. The receiver operating characteristic (ROC) and prediction-area (P-A) curves were used for performance evaluation and showed that the two algorithms had an obvious optimization effect. The BA and FA differentiated in improving multilayer perceptron (MLP), AdaBoost and one-class support vector machine (OCSVM) models; thus, there was no optimization algorithm that was consistently superior to the other. However, the accuracy of the machine learning models was significantly enhanced after optimizing the hyperparameters. The area under curve (AUC) values of the ROC curve of the optimized machine learning models were all higher than 0.8, indicating that the hyperparameter optimization calculation was effective. In terms of individual model improvement, the accuracy of the FA-AdaBoost model was improved the most significantly, with the AUC value increasing from 0.8173 to 0.9597 and the prediction/area (P/A) value increasing from 3.156 to 10.765, where the mineral targets predicted by the model occupied 8.63% of the study area and contained 92.86% of the known mineral deposits. The targets predicted by the improved machine learning models are consistent with the metallogenic geological characteristics, indicating that the swarm intelligence optimization algorithm combined with the machine learning model is an efficient method for mineral prospectivity mapping.

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Recognition of diseased Pinus trees in UAV images using deep learning and AdaBoost classifier

Cited by 88 publications

References 21 publications

Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research

Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research

The prediction of mortality influential variables in an intensive care unit: a case study

A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

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