Analysis of Classification Algorithms for Insect Detection using MATLAB

Rathore, Dileep Singh; Ram, Babu; Pal, Babita; Malviya, Sunil

doi:10.2139/ssrn.3350283

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…The same verdict was obtained by Mohd Johari et al [29], in differentiating the four larval instar stages with an accuracy of 91% to 95%. In addition, Rathore et al [40] also found that KNN, using weighted kernel, achieved a high accuracy of 90% compared to other kernels in distinguishing between various type of insects and between adult and larvae insect sounds.…”

Section: Discussionmentioning

confidence: 99%

“…The same verdict was obtained by Mohd Johari et al [29], in differentiating the four larval instar stages with an accuracy of 91% to 95%. In addition, Rathore et al [40] In general, the best combination for the model to perform well in classifying the healthy and low levels of infestation and achieving a 100% F1 score was the combination of NDVI and GNDVI. For instance, 11 out of 19 models achieved a 100% F1 score in healthy level (Figure 9a), followed by the combination of NDRE and GNDVI, where only six models gained a 100% F1 score.…”

Section: Discussionmentioning

confidence: 99%

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Detection of Bagworm Infestation Area in Oil Palm Plantation Based on UAV Remote Sensing Using Machine Learning Approach

Johari,

Khairunniza-Bejo,

Shariff

et al. 2023

Agriculture

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Due to its rapid reproduction rate and brief life cycle, the most well-known oil palm pest, Metisa plana (Lepidoptera: Psychidae), also known as the bagworm, can spread to epidemic proportions. The outbreak can significantly reduce oil palm yield by resulting in 40% crop losses and 10% to 13% leaf defoliation. A manual census was conducted to count the number of pests and determine the category of infestation; however, when covering a large area, it typically takes more time and labour. Therefore, this study used unmanned aerial vehicles (UAVs) as a quick way to detect the severity levels of infestation in oil palm plantations, including healthy (zero), low, mild, and severe infestation using DJI Inspire 2 with Micasense Altum-PT multispectral camera at an altitude of 70 m above ground. Three combinations were created from the most significant vegetation indices: NDVI and NDRE, NDVI and GNDVI, and NDRE and GNDVI. According to the results, the best combination in classifying healthy and low levels was found to be NDVI and GNDVI, with 100% F1 score. In addition, the combination of NDVI and NDRE was found to be the best combination in classifying mild and severe level. The most important vegetation index that could detect every level of infestation was NDVI. Furthermore, Weighted KNN become the best model that constantly gave the best performance in classifying all the infestation levels (F1 score > 99.70%) in all combinations. The suggested technique is crucial for the early phase of severity-level detection and saves time on the preparation and operation of the control measure.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Detection of Bagworm Infestation Area in Oil Palm Plantation Based on UAV Remote Sensing Using Machine Learning Approach

Johari,

Khairunniza-Bejo,

Shariff

et al. 2023

Agriculture

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“…Insects produce a wide range of sounds, ranging from their eating, moving, wing-beating during flight, and these sounds can be used as unique classifiers to classify specific insect classes. Fine Gaussian SVM and KNN algorithms build on numerous insect sounds 18 are able to classify some insects classes, whereas, another Bayesian model for insect flight sounds 19 showed improved performance for insect classification tasks. Besides, some ANN models such as Probabilistic Neural Network 20 and deep learning algorithms such as CNNs 21 , were also been used in insect sounds classification and detection.…”

Section: Introductionmentioning

confidence: 99%

A ResNet attention model for classifying mosquitoes from wing-beating sounds

Wei

Hossain

Ahmed

2022

Sci Rep

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Mosquitoes are vectors of numerous deadly diseases, and mosquito classification task is vital for their control programs. To ease manual labor and time-consuming classification tasks, numerous image-based machine-learning (ML) models have been developed to classify different mosquito species. Mosquito wing-beating sounds can serve as a unique classifier for mosquito classification tasks, which can be adopted easily in field applications. The current study aims to develop a deep neural network model to identify six mosquito species of three different genera, based on their wing-beating sounds. While existing models focused on raw audios, we developed a comprehensive pre-processing step to convert raw audios into more informative Mel-spectrograms, resulting in more robust and noise-free extracted features. Our model, namely ’Wing-beating Network’ or ’WbNet’, combines the state-of-art residual neural network (ResNet) model as a baseline, with self-attention mechanism and data-augmentation technique, and outperformed other existing models. The WbNet achieved the highest performance of 89.9% and 98.9% for WINGBEATS and ABUZZ data respectively. For species of Aedes and Culex genera, our model achieved 100% precision, recall and F1-scores, whereas, for Anopheles, the WbNet reached above 95%. We also compared two existing wing-beating datasets, namely WINGBEATS and ABUZZ, and found our model does not need sophisticated audio devices, hence performed better on ABUZZ audios, captured on usual mobile devices. Overall, our model has potential to serve in mosquito monitoring and prevalence studies in mosquito eradication programs, along with potential implementation in classification tasks of insect pests or other sound-based classifications.

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“…Artificial neural networks (ANN) are computer models of brain neuron linkage processes that combine weighted inputs from observational data, e.g., acoustic signal pulses, and produce a single binary output that learns its correct value from the observational inputs by use of backpropagation or other methods [ 161 ]. Machine learning incorporates neural networks, including convolutional neural networks (CNN) [ 162 ], and probabilistic neural networks (PNN), perceptual learning prediction (PLP), decision trees and forests, hidden Markov models (HMM), support vector machines (SVM), Bayesian classifiers, and other methods to improve its predictions automatically through experience with datasets where the target insect species have been independently identified [ 163 , 164 , 165 ]. Deep learning [ 156 ] is a machine learning method that incorporates multiple layers of neural networks, each of which extracts specific features or learned representations of input data [ 164 ].…”

Section: Introductionmentioning

confidence: 99%

“…(e) Classification algorithms. Subband-based Cepstral Coefficients (SBC) [ 170 ], Linear Predictive Cepstral Coefficients (LPCCs) and Mel-Frequency Cepstral Coefficients (MFCCs) [ 171 ], as well as wavelets [ 172 , 173 ], KNeighbors [ 16 ] classifiers and similar Medium and Complex tree classifications [ 165 ] have been used also to distinguish target signals from other signals.…”

Section: Introductionmentioning

confidence: 99%

Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management

Mankin

Hagstrum

Guo

et al. 2021

Insects

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Acoustic technology provides information difficult to obtain about stored insect behavior, physiology, abundance, and distribution. For example, acoustic detection of immature insects feeding hidden within grain is helpful for accurate monitoring because they can be more abundant than adults and be present in samples without adults. Modern engineering and acoustics have been incorporated into decision support systems for stored product insect management, but with somewhat limited use due to device costs and the skills needed to interpret the data collected. However, inexpensive modern tools may facilitate further incorporation of acoustic technology into the mainstream of pest management and precision agriculture. One such system was tested herein to describe Sitophilus oryzae (Coleoptera: Curculionidae) adult and larval movement and feeding in stored grain. Development of improved methods to identify sounds of targeted pest insects, distinguishing them from each other and from background noise, is an active area of current research. The most powerful of the new methods may be machine learning. The methods have different strengths and weaknesses depending on the types of background noise and the signal characteristic of target insect sounds. It is likely that they will facilitate automation of detection and decrease costs of managing stored product insects in the future.

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Analysis of Classification Algorithms for Insect Detection using MATLAB

Cited by 7 publications

References 10 publications

Detection of Bagworm Infestation Area in Oil Palm Plantation Based on UAV Remote Sensing Using Machine Learning Approach

Detection of Bagworm Infestation Area in Oil Palm Plantation Based on UAV Remote Sensing Using Machine Learning Approach

A ResNet attention model for classifying mosquitoes from wing-beating sounds

Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management

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