Imagistic Technique and Fractal Analysis - Investigations Mechanisms of the Morphological and Temporal Variability of the Wheat Cultures

The dynamic analysis of the vegetal carpet and of the vegetal cultures, presenting high interest for the management of natural balances and of agricultural systems efficiency. The fractal analysis has been used for the assessment of the morphological and temporal variability within the Triticum aestivum L. ssp. Vulgare species, in four cultivars of non-awned wheat (Avenue, Pitbull, GK Koros and Illico) and 6 awned cultivars (GK Rozi, PG 101, GK Bekes, GK 102, Antonius și Stefanus). The study has been performed in three different vegetation stages: Growth stage 3 - Stem elongation, code 33 BBCH; Growth stage 6 - Flowering, anthesis, code 69 BBCH,and Growth stage 9 - Senescence, code 99 BBCH. The fractal dimensions have been between 1.879 ± 0.067 and 1.963 ± 0.046. From the perspective of the variability induced by cultivars, two distinct clusters have resulted, GNA cluster including the non-awned cultivars (C1-C4) and a second cluster, GA including the awned cultivars (C5-C10) and subclusters depending on the similarity of cultivars. From the point of view of the temporal variability the obvious difference of Growth stage 9 (99 BBCH code), with the variation coefficient within the fractal dimenssion CV9 = 1.448 ± 0.005 (p < 0.01). The fractal dimension obtained using the digital images can be useful for the dissociation of awned / non-awneed cultivars, but only the higher stages of wheat development. It is also useful for the delimitation of wheat development stages

Fractal scaling of apparent soil moisture estimated from vertical planes of Vertisol pit images

Image analysis could be a useful tool for investigating the spatial patterns of apparent soil moisture at multiple resolutions. The objectives of the present work were (i) to define apparent soil moisture patterns from vertical planes of Vertisol pit images and (ii) to describe the scaling of apparent soil moisture distribution using fractal parameters. Twelve soil pits (0.70 m long × 0.60 m width × 0.30 m depth) were excavated on a bare Mazic Pellic Vertisol. Six of them were excavated in April/2011 and six pits were established in May/2011 after 3 days of a moderate rainfall event. Digital photographs were taken from each Vertisol pit using a Kodak™ digital camera. The mean image size was 1600 × 945 pixels with one physical pixel ≈373 μm of the photographed soil pit. Each soil image was analyzed using two fractal scaling exponents, box counting (capacity) dimension (DBC) and interface fractal dimension (Di), and three prefractal scaling coefficients, the total number of boxes intercepting the foreground pattern at a unit scale (A), fractal lacunarity at the unit scale (Λ1) and Shannon entropy at the unit scale (S1). All the scaling parameters identified significant differences between both sets of spatial patterns. Fractal lacunarity was the best discriminator between apparent soil moisture patterns. Soil image interpretation with fractal exponents and prefractal coefficients can be incorporated within a site-specific agriculture toolbox. While fractal exponents convey information on space filling characteristics of the pattern, prefractal coefficients represent the investigated soil property as seen through a higher resolution microscope. In spite of some computational and practical limitations, image analysis of apparent soil moisture patterns could be used in connection with traditional soil moisture sampling, which always renders punctual estimate

Biocomplexity and Fractality in the Search of Biomarkers of Aging and Pathology: Focus on Mitochondrial DNA and Alzheimer's Disease

Alzheimer's disease (AD) represents one major health concern for our growing elderly population. It accounts for increasing impairment of cognitive capacity followed by loss of executive function in late stage. AD pathogenesis is multifaceted and difficult to pinpoint, and understanding AD etiology will be critical to effectively diagnose and treat the disease. An interesting hypothesis concerning AD development postulates a cause-effect relationship between accumulation of mitochondrial DNA (mtDNA) mutations and neurodegenerative changes associated with this pathology. Here we propose a computerized method for an easy and fast mtDNA mutations-based characterization of AD. The method has been built taking into account the complexity of living being and fractal properties of many anatomic and physiologic structures, including mtDNA. Dealing with mtDNA mutations as gaps in the nucleotide sequence, fractal lacunarity appears a suitable tool to differentiate between aging and AD. Therefore, Chaos Game Representation method has been used to display DNA fractal properties after adapting the algorithm to visualize also heteroplasmic mutations. Parameter β from our fractal lacunarity method, based on hyperbola model function, has been measured to quantitatively characterize AD on the basis of mtDNA mutations. Results from this pilot study to develop the method show that fractal lacunarity parameter β of mtDNA is statistically different in AD patients when compared to age-matched controls. Fractal lacunarity analysis represents a useful tool to analyze mtDNA mutations. Lacunarity parameter β is able to characterize individual mutation profile of mitochondrial genome and appears a promising index to discriminate between AD and aging

Penggabungan fitur dimensi fraktal dan lacunarity untuk klasifikasi daun

Tanaman memegang peranan penting dalam kehidupan manusia dan makhluk hidup lainnya. Dengan semakin tingginya keanekaragaman spesies tanaman di dunia, sulit untuk mengidentifikasi atau mengklasifikasi tanaman secara manual melalui pengamatan langsung. Perkembangan penelitian di bidang pengolahan citra digital telah membuka kesempatan luas bagi banyak peneliti di berbagai bidang penelitian untuk mengklasifikasi tanaman secara cepat dan otomatis. Daun merupakan bagian pada tanaman yang paling sering digunakan dalam klasifikasi tanaman, baik secara manual maupun otomatis. Melalui pengamatan pada daun, beberapa karakteristik bisa diperoleh; di antaranya adalah bentuk pinggiran daun, bentuk urat daun serta tekstur daun. Banyak objek-objek di alam memiliki sifat yang mirip fraktal, dimana terdapat pola yang berulang pada skala tertentu, termasuk pada objek seperti daun. Dimensi fraktal merupakan deskriptor fitur bentuk maupun tekstur yang telah banyak diterapkan pada berbagai bidang penelitian karena mampu mendeskripsikan kompleksitas sebuah objek dalam bentuk dimensi pecahan. Sementara itu, lacunarity, merupakan deskriptor fitur tekstur yang mampu menunjukkan seberapa heterogen suatu citra tekstur. Namun lacunarity belum cukup dieksplorasi dalam banyak bidang penelitian dan belum ada penelitian signifikan yang mencoba menggabungkan fitur dimensi fraktal dengan lacunarity dalam penelitian yang berfokus pada klasifikasi citra digital daun. Pada penelitian ini, diajukan penerapan konsep fraktal dalam menyelesaikan masalah klasifikasi daun dengan berfokus pada penggabungan fitur dimensi fraktal dan lacunarity. Ekstraksi fitur bentuk pinggiran dan tulang daun dilakukan melalui perhitungan dimensi fraktal dengan menerapkan metode box counting. Sedangkan fitur hasil perhitungan nilai lacunarity diperoleh melalui proses ekstraksi fitur tekstur daun dengan menerapkan metode gliding box. Menggunakan 626 dataset dari flavia, pengujian dilakukan dengan menganalisis performa dari dimensi fraktal dan lacunarity ketika digunakan secara terpisah dan ketika dikombinasikan satu sama lain dalam memperbaiki hasil klasifikasi daun dari metode fraktal sebelumya, serta dengan mempertimbangkan parameter ukuran kotak r yang paling optimal. Hasil uji coba dengan pengklasifikasi support vector machine menunjukkan bahwa penggabungan fitur dimensi fraktal dan lacunarity mampu meningkatkan akurasi klasifikasi hingga 93.92 % ============================================================================================ Plant plays an important role in the existence of all beings in the world. With the high diversity in plant species, it is hard to classify plant manually only by observing their properties. The development of study in digital image processing opened a wide chance for many researches from various area of study to quickly and automatically classify plant species. Plant leaf was the main properties that commonly used in plant classification whether it is manually or automatically. By looking at plant leaf, some unique characteristics can be obtained; between them were leaf contour shape, leaf vein shape, and leaf surface texture. There are many natural objects and phenomenons that have characteristic of fractals, like a pattern that repeated in a certain scale, including natural objects like plant leaf. Fractal dimension was a widely known feature descriptor for shape or texture that able to describe the complexity of an object in a form of fractional dimension’s value. On the other hand, lacunarity is a feature descriptor that able to describe the heterogeneity of a texture image. However, lacunarity was not really exploited in many fields and there are no significant efforts that trying to combine fractal dimension and lacunarity in the study of automatic plant leaf classification. In this study, a fractal concept and its performances in leaf classification will be analyzed by using two fractal based feature: fractal dimension and lacunarity. We focused on how to extract the two features and combine them for a better classification result. A box counting approach is implemented to get the fractal dimension feature vectors of leaf contour and vein, while an improved gliding box algorithm is implemented to get the lacunarity feature vectors of leaf texture. By combining this two feature, a feature vectors that highly represents the unique feature of each leaf is then expected to be obtained. Using 626 leaf images from flavia, experiment was conducted by separately or jointly analyzing the performace of both fractal dimension feature vectors and lacunarity feature vectors, while considering the optimal box size r. Using support vector machine classifier, result shows that combination between fractal dimension and lacunarity was able to increase the classification accuracy up to 93.92%

Fitur Berbasis Fraktal dari Koefisien Wavelet untuk Klasifikasi Citra Daun

Semakin banyak dan beragamnya jenis tanaman di dunia mengakibatkan semakin sulit untuk mengidentifikasi dan mengklasifikasi tanaman secara manual. Daun merupakan bagian dari tanaman yang sering dipakai untuk identifikasi dan klasifikasi tanaman. Metode klasifikasi daun secara automatis telah banyak dikembangkan oleh para peneliti. Pada penelitian sebelumnya sistem klasifikasi daun otomatis dibangun menggunakan fitur berbasis fraktal yaitu dimensi fraktal dan lacunarity. Dimensi fraktal dapat diterapkan sebagai deskriptor fitur bentuk sebuah obyek. Lacunarity adalah deskriptor tekstur yang menggambarkan seberapa heterogen suatu citra sehingga dapat digabungkan dengan deskriptor bentuk dimensi fraktal untuk sistem klasifikasi daun otomatis. Sistem klasifikasi daun otomatis berbasis dimensi fraktal dan lacunarity dapat mengklasifikasi daun dengan akurasi tinggi namun memerlukan banyak langkah preprocessing sehingga mengakibatkan komputasi sistem meningkat. Pada penelitian ini diusulkan penggabungan metode praproses berbasis wavelet dengan fitur berbasis fraktal. Ekstraksi fitur menggunakan praproses teknik dekomposisi wavelet sehingga tidak memerlukan banyak langkah preprocessing sehingga komputasi menjadi lebih ringan. Ekstraksi fitur dengan strategi kombinasi tersebut diharapkan mampu meningkatkan akurasi dan mengurangi kompleksitas komputasi sistem klasifikasi daun. Penelitian ini dilakukan melalui beberapa fase, yang pertama adalah praproses menggunakan teknik wavelet untuk memperoleh urat dan tekstur daun. Ekstraksi fitur tekstur daun dilakukan melalui perhitungan lacunarity. Ekstraksi fitur bentuk pinggiran dan tulang daun dilakukan melalui perhitungan dimensi fraktal. Uji coba dilakukan menggunakan 626 citra dari dataset flavia. Pengujian dilakukan dengan menganalisis performa dari fitur berbasis fraktal (lacunarity dan dimensi fraktal) dari koefisien wavelet ketika digunakan secara terpisah dan ketika dikombinasikan satu sama lain dalam memperbaiki hasil klasifikasi daun. Pengujian dilakukan dengan menggunakan klasifier Support Vector Machine (SVM). Hasil eksperimen menunjukkan bahwa metode ekstraksi fitur statistik pada dekomposisi wavelet lebih unggul dalam akurasi dan waktu komputasi dibandingkan dengan metode ekstraksi fitur berbasis fraktal dari penelitian sebelumnya dengan akurasi 96.66% dan waktu komputasi 329.33 detik. ================================================================= The more numerous and varied types of plants in the world make it more difficult to identify and classify plants manually. The leaves are part of the plant that is often used for plant identification and classification. Automatic leaf classification method has been developed by many researchers. In the previous research the automatic leaf classification system was built using fractal-based features of fractal dimension and lacunarity. Fractal dimensions can be applied as feature descriptor forms of an object. Lacunarity is a texture descriptor that describes how heterogeneous an image can be combined with a fractal dimensional form descriptor for automatic leaf classification systems. Automatic leaf classification system based on fractal dimensions and lacunarity can classify leaves with high accuracy but requires a lot of preprocessing steps resulting in increased system computation. In this study proposed incorporation of wavelet based praprocess method with fractal based feature. Feature extraction uses wavelet decomposition process preprocessing so it does not require many preproce ssing steps so computing becomes lighter. Feature extraction with such a combination strategy is expected to improve accuracy and reduce the computational complexity of leaf classification systems. This research is done through several phases, the first is a pre-process using wavelet technique to obtain leaf veins and texture. Leaf texture feature extraction is done through lacunarity calculations. The feature extraction of peripheral shape and bone of leaves is done by calculating fractal dimensions. Using 626 datasets from flavia, testing was performed by analyzing the performance of fractal-based features (lacunarity and fractal dimensions) of wavelet coefficients when used separately and when combined with each other in improving leaf classification results. Testing is done by using Classification Support Vector Machine (SVM). The experimental results show that the statistical feature extraction method on wavelet decomposition is superior in accuracy and computation time compared to the fractal-based feature extraction method from the previous study with 96.66% accuracy and 329.33 second computational time