Coconut Maturity Recognition Using Convolutional Neural Network

Parvathi, S.; Sankar, Tamil Selvi

doi:10.1007/978-981-16-9991-7_7

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…Models that are widely used for image classification due to their good results. For example, the work of Subramanian and Sankar (2022), where they compare this CNN model and others for coconut maturity detection. Or the work of Sehree and Khidhir ( 2022) that classifies olive trees from unmanned aerial vehicle images.…”

Section: Machine Learning and Deep Leaning Resultsmentioning

confidence: 99%

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Machine Learning and Deep Leaning Resultsmentioning

confidence: 99%

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

2024

Frontiers Research Topics

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“…Models that are widely used for image classification due to their good results. For example, the work of Subramanian and Sankar (2022) , where they compare this CNN model and others for coconut maturity detection. Or the work of Sehree and Khidhir (2022) that classifies olive trees from unmanned aerial vehicle images.…”

Section: Resultsmentioning

confidence: 99%

Remote fruit fly detection using computer vision and machine learning-based electronic trap

Molina-Rotger,

Morán,

Miranda

et al. 2023

Front. Plant Sci.

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IntroductionIntelligent monitoring systems must be put in place to practice precision agriculture. In this context, computer vision and artificial intelligence techniques can be applied to monitor and prevent pests, such as that of the olive fly. These techniques are a tool to discover patterns and abnormalities in the data, which helps the early detection of pests and the prompt administration of corrective measures. However, there are significant challenges due to the lack of data to apply state of the art Deep Learning techniques.MethodsThis article examines the detection and classification of the olive fly using the Random Forest and Support Vector Machine algorithms, as well as their application in an electronic trap version based on a Raspberry Pi B+ board.ResultsThe combination of the two methods is suggested to increase the accuracy of the classification results while working with a small training data set. Combining both techniques for olive fly detection yields an accuracy of 89.1%, which increases to 94.5% for SVM and 91.9% for RF when comparing all fly species to other insects.DiscussionThis research results reports a successful implementation of ML in an electronic trap system for olive fly detection, providing valuable insights and benefits. The opportunities of using small IoT devices for image classification opens new possibilities, emphasizing the significance of ML in optimizing resource usage and enhancing privacy protection. As the system grows by increasing the number of electronic traps, more data will be available. Therefore, it holds the potential to further enhance accuracy by learning from multiple trap systems, making it a promising tool for effective and sustainable fly population management.

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“…Promising results are obtained with respect to classification accuracy and F-score, which had the best performances achieved by the RF classifier. Reference [2] addresses finding a robust deep learning scheme to automatically recognize the coconut maturity in different environmental conditions using real images taken from coconut farms and Google. Different CNN models have been used to recognize coconuts as tender coconut and mature coconut.…”

Section: Introductionmentioning

confidence: 99%