WATERMELON PLANT CLASSIFICATION BASED ON SHAPE AND TEXTURE FEATURE LEAF USING SUPPORT VECTOR MACHINE (SVM)

Nowadays, some efforts are used to increase results of agriculture production. One of those is utilizing herbisides to exterminate the weeds. However, there are some of the weeds having resemblance with the plant, with the result that we need to classify the plant and the weeds before utilizing herbisides as an extermination weeds. In this paper, we use watermelon plant classification as case study. The recognition of the plant owned by the similarity of leaves of these plants are divided into three phases. At the first phase we perform preprocessing to convert the RGB image into a grayscale images. Further, the grayscale images are changed into segmentation of edge detection using Canny operator. In the second, we use feature extraction to retrieve important informations for the recognition of those leaves. The last phase we classify that leaves as watermelon plants or weeds using Support Vector Machine (SVM) algorithm. The results of early trials indicate that this method has an accuracy of 91,3%. Keywords : image, leaf, edge detection, feature extraction, and plant classification esults of early trial

Klasifikasi Penyakit Daun Bayam Dengan Menggunakan Metode Support Vector Machine (SVM)

The classification of leaf diseases in amaranth plants provides a promising step towards sustainable food security in agriculture. Production Costs can also be significantly increased if plant diseases are not detected and cured in the early stages. Support Vector Machine (SVM) is an algorithm that can classify the types of diseases in spinach leaves. The image was taken using a smartphone as many as 1426 images divided into 3 classes. The class in this study represented 2 types of diseases in spinach leaf plants, namely hollow disease, and rust disease. This study proposes a classification of diseases in the leaves of amaranth plants based on the texture features of the Grey Level Co-occurrence Matrix. then carried out the classification of amaranth leaf disease using the support vector machine (svm) method. The results of the experiment successfully classified between hollow spinach leaf disease and rust spinach leaf disease using the Support Vector Machine (SVM), the correct recognition rate of the training data was 54.6293 percent, and the correct recognition rate of the image test was 57.2614percent.   Keywords— Keywords: Spinach Leaf Disease Classification, Support Vector Machine (SVM), Grey Level Co-occurrence Matrix (GLCM).Abstrak Klasifikasi penyakit daun pada tanaman bayam memberikan langkah yang menjanjikan menuju ketahanan pangan yang berkelanjutan pada bidang pertanian. Biaya Produksi pun bisa signifikan meningkat jika penyakit tanaman tidak terdeteksi dan disembuhkan pada tahap awal. Support Vector Machine (SVM) adalah suatu algoritma yang dapat mengklasifikasikan jenis penyakit pada daun bayam. Citra diambil dengan menggunakan smartphone sebanyak 1426 citra yang terbagi menjadi 2 kelas. Kelas pada penelitian ini mewakilkan 2 jenis penyakit pada tanaman daun bayam yaitu penyakit bolong, dan penyakit karat. Penelitian ini mengusulkan klasifikasi penyakit pada daun tanaman bayam berdasarkan fitur tekstur Grey Level Co-occurrence Matrix. Kemudian melakukan klasifikasi penyakit daun bayam dengan menggunakan metode Support Vector Machine (SVM). Hasil dari percobaan berhasil mengklasifikasi antara penyakit daun bayam yang bolong dan penyakit daun bayam yang karat dengan menggunakan Support Vector Machine (SVM), Tingkat pengenalan yang benar dari training data adalah 54,6293 persen, dan tingkat pengenalan yang benar dari uji gambar adalah 57,2614 persen.. Pada era ini, banyak sekali aplikasi yang tersedia di berbagai media online seperti di testing dan user testing. aplikasi pembelejaran ini dapat berjalan dengan semestinya. Serta hasil pengujian kepada pengguna juga mendapat nilai ketertarikan pengguna terhadap suatu aplikasi pembelajaran.   Kata kunci— Kata kunci: Klasifikasi Penyakit daun bayam, Support Vector Machine (SVM), Grey Level Co-occurrence Matrix (GLCM

Soybean leaf disease detection and severity measurement using multiclass SVM and KNN classifier

Soybean fungal diseases such as Blight, Frogeye leaf spot and Brown Spot are a significant threat to soybean plant due to the severe symptoms and lack of treatments. Traditional diagnosis of the thease diseases relies on disease symptom identification based on neaked eye observation by pathalogiest, which can lead to a high rate of false-recognition. This work present a novel system, utilizing multiclass support vector machine and KNN classifiers, for detection and classification of soybean diseases using color images of diseased leaf samples. Images of healthy and diseased leaves affected by Blight, Frogeye leaf spot and Brown Spot were acquired by a digital camera. The acquired images are preprocessed using image enhancement techniques. The background of each image was removed by a thresholding method and the Region of Interest (ROI) is obtained. Color-based segmentation technique based on K-means clustering is applied to the region of interest for partitioning the diseased region. The severity of disease is estimated by quantifying a number of pixels in the diseased region and in total leaf region. Different color features of segmented diseased leaf region were extracted using RGB color space and texture features were extracted using Gray Level Co-occurrence Matrix (GLCM) to compose a feature database. Finally, the support vector machine (SVM) and K-Nearest Negbiour (KNN) classifiers are used for classifying the disease. This proposed classifers system is capable to classify the types of blight, brown spot, frogeye leaf spot diseases and Healthy samples with an accuracy of 87.3% and 83.6 % are achieved

A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

In this paper, we employ Probabilistic Neural Network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition algorithm. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.Comment: 6 pages, 3 figures, 2 table

Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses

Image analysis for ornamental crops is discussed with examples from the bedding plant industry. Feed-forward artificial neural networks are used to segment top and side view images of three contrasting species of bedding plants. The segmented images provide objective measurements of leaf and flower cover, colour, uniformity and leaf canopy height. On each imaging occasion, each pack was scored for quality by an assessor panel and it is shown that image analysis can explain 88.5%, 81.7% and 70.4% of the panel quality scores for the three species, respectively. Stereoscopy for crop height and uniformity is outlined briefly. The methods discussed here could be used for crop grading at marketing or for monitoring and assessment of growing crops within a glasshouse during all stages of production

ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network

In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems, such as leaf segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model for the task at hand. Despite the recent release of a few plant phenotyping datasets, large annotated plant image datasets for the purpose of training deep learning algorithms are lacking. One common approach to alleviate the lack of training data is dataset augmentation. Herein, we propose an alternative solution to dataset augmentation for plant phenotyping, creating artificial images of plants using generative neural networks. We propose the Arabidopsis Rosette Image Generator (through) Adversarial Network: a deep convolutional network that is able to generate synthetic rosette-shaped plants, inspired by DCGAN (a recent adversarial network model using convolutional layers). Specifically, we trained the network using A1, A2, and A4 of the CVPPP 2017 LCC dataset, containing Arabidopsis Thaliana plants. We show that our model is able to generate realistic 128x128 colour images of plants. We train our network conditioning on leaf count, such that it is possible to generate plants with a given number of leaves suitable, among others, for training regression based models. We propose a new Ax dataset of artificial plants images, obtained by our ARIGAN. We evaluate this new dataset using a state-of-the-art leaf counting algorithm, showing that the testing error is reduced when Ax is used as part of the training data.Comment: 8 pages, 6 figures, 1 table, ICCV CVPPP Workshop 201