Computer aided plant identification system

Pham, Ngoc-Hai; Le, Thi‐Lan; Grard, Pierre; Nguyen, Van-Ngoc

doi:10.1109/commantel.2013.6482379

Cited by 21 publications

(17 citation statements)

References 3 publications

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“…To make classification more efficient, four color features ('mean', 'standard deviation', 'kurtosis', 'skewness') are extracted along with five texture features. Majority of the previous studied have used only shape features [8,11,12,[15][16][17][18] for plant identification. This study however, emphasises on texture and color features because shape features cannot always correctly identify a plant.…”

Section: Resultsmentioning

confidence: 99%

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Plant Species Identification based on Plant Leaf Using Computer Vision and Machine Learning Techniques

Kaur¹,

Kaur²

2019

J Multimed Inf Syst

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Plants are very crucial for life on Earth. There is a wide variety of plant species available, and the number is increasing every year. Species knowledge is a necessity of various groups of society like foresters, farmers, environmentalists, educators for different work areas. This makes species identification an interdisciplinary interest. This, however, requires expert knowledge and becomes a tedious and challenging task for the non-experts who have very little or no knowledge of the typical botanical terms. However, the advancements in the fields of machine learning and computer vision can help make this task comparatively easier. There is still not a system so developed that can identify all the plant species, but some efforts have been made. In this study, we also have made such an attempt. Plant identification usually involves four steps, i.e. image acquisition, pre-processing, feature extraction, and classification. In this study, images from Swedish leaf dataset have been used, which contains 1,125 images of 15 different species. This is followed by pre-processing using Gaussian filtering mechanism and then texture and color features have been extracted. Finally, classification has been done using Multiclass-support vector machine, which achieved accuracy of nearly 93.26%, which we aim to enhance further.

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Section: Resultsmentioning

confidence: 99%

“…Pham et al [11] in their computer-aided plant identification system compared the performance of two feature descriptors i.e. histogram of oriented gradients (HOG) and Hu moments.…”

Section: Literature Reviewmentioning

confidence: 99%

Plant Species Identification based on Plant Leaf Using Computer Vision and Machine Learning Techniques

Kaur¹,

Kaur²

2019

J Multimed Inf Syst

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“…We obtained an average accuracy of 96.48% for our data set. We compared the accuracy of our system with other systems in Table I, based on DMF and PNN [4], SURF with SVM [17], HOG with SVM [21] and FFD with k-NN [22].…”

Section: Resultsmentioning

confidence: 99%

“…The classification framework use SIFT features in [4] 90.31% SURF SVM [17] 95.94% Method proposed in [23] 71.4% SVM+HOG [21] 84.68% FFD+ kNN [22] 95.66% Our method 96.48% Fig. 7.…”

Section: Discussionmentioning

confidence: 99%

Computer assisted plant identification system for Android

Priyankara

Withanage

2015

2015 Moratuwa Engineering Research Conference (MERCon)

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Plant leaves provide sufficient features to distinguish them among other species. Identification of plants using leaf images is a classic problem in digital image processing. Usually those image processing systems use shape based digital morphological features for leaf identification task. Even there are number of studies on leaf based plant identification, very few of them are for mobiles. In this paper we describe a leaf image based plant identification system using SIFT features combining with Bag Of Word (BOW) model and Support Vector Machine (SVM) classifier. The system is trained to classify 20 species and obtained 96.48% accuracy level. Based on the results, we developed an Android application communicates with the server and gives users the ability to identify plant species using photographs taken of plant leaves using the smart phone.

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“…Another substantially studied local feature approach is the histogram of oriented gradients (HOG) descriptor (Pham, Le, Grard, & Nguyen, 2013). The HOG descriptor, introduced by (Lowe, 2004) is similar to SIFT, except that it uses an overlapping local contrast normalization across neighbouring cells grouped into a block.…”

Section: Introductionmentioning

confidence: 99%

Plant species identification from leaf patterns using histogram of oriented gradients feature space and convolution neural networks

Bao

Kiet

Dinh

et al. 2019

Journal of Information and Telecommunication

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The determination of plant species from field observation requires substantial botanical expertise, which puts it beyond the reach of most nature enthusiasts. Traditional plant species identification is almost impossible for the general public and challenging even for professionals that deal with botanical problems daily, such as, conservationists, farmers, foresters, and landscape architects. Even for botanists themselves, species identification is often a difficult task. In this research, we proposed using two methods for the problem of plant species identification from leaf patterns. Firstly, we use a traditional recognition shallow architecture with extracted features histogram of oriented gradients (HOG) vector, then those features used to classifying by SVM algorithm. Secondly, we apply a deep convolutional neural network (CNN) for recognition purpose. We experimented on leaves data set in the Flavia leaf data set and the Swedish leaf data set. We want to compare a tradition method and a method consider as current state-of-the-art.

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Computer aided plant identification system

Cited by 21 publications

References 3 publications

Plant Species Identification based on Plant Leaf Using Computer Vision and Machine Learning Techniques

Plant Species Identification based on Plant Leaf Using Computer Vision and Machine Learning Techniques

Computer assisted plant identification system for Android

Plant species identification from leaf patterns using histogram of oriented gradients feature space and convolution neural networks

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