Recognition of plant leaf image based on fractal dimension features

Du, Ji-Xiang; Zhai, Chuan-Min; Wang, Qingping

doi:10.1016/j.neucom.2012.03.028

Cited by 95 publications

(49 citation statements)

References 8 publications

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“…GLVQ has proposed by A. Sato Yamada, using steepest descent method which minimizes a cost function to defined the codebook or prototype vectors update [14]. Relative distance difference is defined as (6) and cost function as (7).…”

Section: Generalized Relevance Learning Vector Quantization (Grlvq)mentioning

confidence: 99%

“…Plant leaf has special character in texture, shape, and color features, and it challenges in digital image processing and pattern recognition study, how to get those feature automatically [5]. Ji-xiang Du et all use fractal feature in their study [6], X Wang using dual scale decomposition for feature extraction, it is based on spatial domain feature extraction [7]. The machine learning study in automatic plant classification also interesting and many researchers have paid interest in this task.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Plant Leaf Recognition Using Competitive Based Learning Algorithm

Imah

Rahayu

Wintarti

2018

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. Plant recognition based on digital leaf image has received as particular attention in computer vision and intelligence system, due its important implication in automatic plant identification. Plant species have the unique leaf characteristics such as the shape, texture, margin, and colour, which different each other. This study presents a novel method for automation plant recognition using Generalized Relevance Learning Vector Quantization (GRLVQ). GRLVQ is a competitive based learning algorithm which is integrating features extraction and classification phases. The experimental result shows that GRLVQ has better performance than the predecessor algorithm.

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Section: Generalized Relevance Learning Vector Quantization (Grlvq)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Plant Leaf Recognition Using Competitive Based Learning Algorithm

Imah

Rahayu

Wintarti

2018

IOP Conf. Ser.: Mater. Sci. Eng.

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“…Du et al [45] have devised a new method of describing the characteristics of plant leaves based on the outline fractal dimension and venation fractal dimension. Multiple direction edge detection method was used to separate leaf edge and vein.…”

Section: Edge and Vein Segmentationmentioning

confidence: 99%

A Review on Diagnosis of Nutrient Deficiency Symptoms in Plant Leaf Image Using Digital Image Processing

Jeyalakshmi¹,

Radha²

2017

IJIVP

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Plants, for their growth and survival, need 13 mineral nutrients. Toxicity or deficiency in any one or more of these nutrients affects the growth of plant and may even cause the destruction of the plant. Hence, a constant monitoring system for tracking the nutrient status in plants becomes essential for increase in production as well as quality of yield. A diagnostic system using digital image processing would diagnose the deficiency symptoms much earlier than human eyes could recognize. This will enable the farmers to adopt appropriate remedial action in time. This paper focuses on the review of work using image processing techniques for diagnosing nutrient deficiency in plants.

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“…Detecting a leaf vein and segmenting it from the leaf objects is quite complicated because of the very low contrast difference between leaf veins and leaf objects [6][7]. We propose to apply the multithresholding method in segment-ing the leaf veins and obtained more than one image of the leaf veins.…”

Section: Preprocessing and Segmentationmentioning

confidence: 99%

“…Therefore, shape and leaf texture are widely studied in automatic plat identification. Some example of leaves shape features have been analyzed in several studies, like geometric descriptors [5] and fractal dimension [6,7]. Examples of research related to leaf texture analyzing is GLCM and LBP [8] and Gabor [9].…”

Section: Introductionmentioning

confidence: 99%

Fractal Dimension and Lacunarity Combination for Plant Leaf Classification

Muchtar

Suciati

Fatichah

2016

Jurnal Ilmu Komputer dan Informasi

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Plants play important roles for the existence of all beings in the world. High diversity of plant's species make a manual observation of plants classifying becomes very difficult. Fractal dimension is widely known feature descriptor for shape or texture. It is utilized to determine the complexity of an object in a form of fractional dimension. On the other hand, lacunarity is a feature descriptor that able to determine the heterogeneity of a texture image. Lacunarity was not really exploited in many fields. Moreover, there are no significant research on fractal dimension and lacunarity combination in the study of automatic plant's leaf classification. In this paper, we focused on combination of fractal dimension and lacunarity features extraction to yield better classification result. A box counting method is implemented to get the fractal dimension feature of leaf boundary and vein. Meanwhile, a gliding box algorithm is implemented to get the lacunarity feature of leaf texture. Using 626 leaves from flavia, experiment was conducted by analyzing the performance of both feature vectors, while considering the optimal box size r. Using support vector machine classifier, result shows that combined features able to reach 93.92 % of classification accuracy. Keywords: leaf classification, fractal dimension, lacunarity, box counting, gliding box AbstrakTumbuhan memegang peranan penting dalam kehidupan manusia. Tingginya keberagaman spesies tumbuhan membuat metode pengamatan manual dalam klasifikasi daun menjadi semakin sulit. Dimensi fraktal merupakan deskriptor bentuk dan tekstur yang mampu mendeskripsikan kompleksitas dari suatu objek dalam bentuk dimensi pecahan. Di sisi lain, lacunarity adalah deskriptor tekstur berbasis fraktal yang mampu mendeskripsikan heterogenitas dari citra tekstur. Namun lacunarity belum cukup dieksplorasi dalam banyak kasus dan belum ada usaha yang cukup signifikan dalam mengkombinasikan dimensi fraktal dan lacunarity dalam bidang klasifikasi tumbuhan secara otomatis. Penelitian ini berfokus pada ekstraksi dan kombinasi fitur dimensi fraktal dan lacunarity untuk meningkatkan akurasi klasifikasi. Metode box counting diterapkan untuk memperoleh dimensi fraktal dari bentuk pinggiran dan urat daun, sementara metode gliding box diterapkan untuk memperoleh fitur lacunariy dari tekstur daun. menggunakan 626 citra daun dari flavia, percobaan dilakukan dengan menganalisis performa dari kedua fitur dengan mempertimbangkan ukuran kotak r yang paling optimal. Klasifikasi dengan support vector machine menunjukkan bahwa hasil kombinasi kedua fitur mampu mencapai rata-rata akurasi hingga 93.92%.

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Recognition of plant leaf image based on fractal dimension features

Cited by 95 publications

References 8 publications

Plant Leaf Recognition Using Competitive Based Learning Algorithm

Plant Leaf Recognition Using Competitive Based Learning Algorithm

A Review on Diagnosis of Nutrient Deficiency Symptoms in Plant Leaf Image Using Digital Image Processing

Fractal Dimension and Lacunarity Combination for Plant Leaf Classification

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