Fuzzy logic applied to detect olive trees in high resolution images

Moreno‐Garcia, Juan; Jiménez, L.; Rodríguez-Benítez, Luis; Solana-Cipres, C.

doi:10.1109/fuzzy.2010.5584310

Cited by 7 publications

(7 citation statements)

References 12 publications

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“…Juan et al utilized the same imagery over the application of fuzzy logic [8] and showed quite similar results as per K-Means technique. The above-mentioned techniques showed accurate results with less processing information, however lacked diversity in terms of the number of images along with the ground classes.…”

Section: Comparative Analysis With Benchmark Schemesmentioning

confidence: 85%

“…The results showed an overall omission rate of zero in six samples and a commission rate of one in six. Other methods utilized fuzzy logic, generating a fuzzy number to detect the olive trees using k-neighbor approach [8]. Promising results were obtained from the methodology showing an omission rate of one in six and a commission rate of zero.…”

Section: To Presentmentioning

confidence: 99%

“…Various basic image pre-processing techniques including image segmentation [3], blob detection [4][5][6], and template matching [7] have been devised for accurate remote detection. Similarly, complex techniques with artificial intelligence enabled [8] and classification-based systems has also been proposed [9,10] to achieve accurate and confident detection results.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Detection System of Olive Trees Using Improved K-Means Algorithm

Waleed

Khan

et al. 2020

Remote Sensing

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Olive cultivation over the past few years has spread across Mediterranean countries with Spain being the world’s largest olive producer among them. Because olives are a major part of the economy for such countries keeping records of their tree count and crop yield is of high significance. Manual counting of trees over such large areas is humanly infeasible. To address this problem, we propose an automatic method for the detection and enumeration of olive trees. The algorithm is a multi-step classification system comprising pre-processing, image segmentation, feature extraction, and classification. RGB satellite images were acquired from the Spanish territory and pre-processed to suppress the additive noise. The region of interest was then segmented from the pre-processed images using K-Means segmentation, through which statistical features were extracted and classified. Promising results were achieved for all classifiers, namely Naive Bayesian, Support Vector Machines (SVMs), Random Forest and Multi-Layer Perceptrons (MLPs), at various division ratios of data samples. In a comparison of all the classification algorithms, Random Forest outperformed the rest by an overall accuracy of 97.5% at the division ratio of 70 to 30 for training to testing.

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Section: Comparative Analysis With Benchmark Schemesmentioning

confidence: 85%

Section: To Presentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Detection System of Olive Trees Using Improved K-Means Algorithm

Waleed

Khan

et al. 2020

Remote Sensing

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“…Juan Moreno et al used the fuzzy logic method to generate the fuzzy number and used it to process the olive trees imagery using k-neighbor approach [16]. The methodology was tested over VHR satellite imagery obtained from SIGPAC (Sistema de Informacion Geografica de parcelas agricolas) viewer of Ministry of Environment and Rural and Marine.…”

Section: Classificationmentioning

confidence: 99%

An Automated Method for Detection and Enumeration of Olive Trees Through Remote Sensing

Waleed

Khan

et al. 2020

IEEE Access

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Country olive forests are one of the major contributors with economic aspects. Spain is the leading country in the world which produced around 45% of olive oil compared to the total production in the world. The value of 2.4 million hectares of land is dedicated to olive cultivation in the country, which made it a huge olive oil producer in the world. Olive crop widely spread over large extensive areas; manual counting of trees is humanly infeasible. To address this problem, we propose an automatic scheme for the detection and enumeration of olive trees. The proposed technique comprises of multi-step image processing techniques applied over a single band of imagery. The single red band once extracted from the color spectrum of acquired images, is then sharpened and edges are detected. The closed edges formed by the tree boundaries are transformed into white blobs using morphological reconstruction. Resulting circular blobs are then filtered out based on their shape and size. Blobs with circular geometry and in-range radius are considered as olive trees, which are then mapped with the existing ground information. Results have been generated over the diverse images capturing the ground truth information with an estimation error of 1.27%.

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“…While hierarchical CA is widely used in olive oil research nonhierarchical (NHCA) is rarely employed. Instead, NHCA methods are heavily used in projects related to remote sensing (e.g., the detection of olive trees in satellite imagery [Moreno‐Garcia, Jiménez Linares, Rodriguez Benitez, Solana‐Cipres, ]) or sensory analysis (Pagliuca & Scarpato, ).…”

Section: Multivariate Proceduresmentioning

confidence: 99%

Chemometrical and molecular methods in olive oil analysis: A review

Avramidou¹,

Doulis²,

Petrakis³

2018

J Food Process Preserv

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Foods based on Olea europaea are of substantial importance for Mediterranean people. The association between olive products with great civilizations emphasizes the role that played the olive tree products and especially the olive oil. This generated the need for authentication, quality control and traceability of olive products. The physicochemical and sensory analysis methods, both classical (discrimination, ordination, classification) and novel (artificial neural networks, fuzzy logic, expert systems, decision trees, support vector machines) supplemented with molecular marker techniques (SSR, AFLP, RAPD), generated the need for effective multivariate analytical methods. These mathematically complex methods are greatly needed in the detection of adulterations of oil products and particularly for olive oil. The lack of mathematical symbolism and jargon is believed to make the text attractive to all potential workers in the field and help them improve the already low level of adulterations and augment the quality control databases for Mediterranean olive products. Practical applications In this review, multivariate methods applied until now in chemometrics supplemented with molecular methods of olive‐based products were presented. Data processing in chemometris and molecular methods is an important issue in order to examine, explain, and evaluate such priorities of olive oil‐based products. This review will facilitate researches to select the best suitable statistic model or equation regarding their hypothesis and use the most appropriate method in order to improve the low level of adulterations and augment in the quality control databases for Mediterranean olive products.

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Fuzzy logic applied to detect olive trees in high resolution images

Cited by 7 publications

References 12 publications

Automatic Detection System of Olive Trees Using Improved K-Means Algorithm

Automatic Detection System of Olive Trees Using Improved K-Means Algorithm

An Automated Method for Detection and Enumeration of Olive Trees Through Remote Sensing

Chemometrical and molecular methods in olive oil analysis: A review

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