Plain, Edge, and Texture Detection Based on Orthogonal Moment

Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOMs). Therefore, finding a fast PET classification method that accurately classify image pattern is crucial. To this end, this paper proposes a new scheme for accurate and fast image pattern classification using an efficient DOM. To reduce the computational complexity of feature extraction, an election mechanism is proposed to reduce the number of processed block patterns. In addition, support vector machine is used to classify the extracted features for different block patterns. The proposed scheme is evaluated by comparing the accuracy of the proposed method with the accuracy achieved by state-of-the-art methods. In addition, we compare the performance of the proposed method based on different DOMs to get the robust one. The results show that the proposed method achieves the highest classification accuracy compared with the existing methods in all the scenarios considered

Brachiaria species identification using imaging techniques based on fractal descriptors

The use of a rapid and accurate method in diagnosis and classification of species and/or cultivars of forage has practical relevance, scientific and trade in various areas of study. Thus, leaf samples of fodder plant species \textit{Brachiaria} were previously identified, collected and scanned to be treated by means of artificial vision to make the database and be used in subsequent classifications. Forage crops used were: \textit{Brachiaria decumbens} cv. IPEAN; \textit{Brachiaria ruziziensis} Germain \& Evrard; \textit{Brachiaria Brizantha} (Hochst. ex. A. Rich.) Stapf; \textit{Brachiaria arrecta} (Hack.) Stent. and \textit{Brachiaria spp}. The images were analyzed by the fractal descriptors method, where a set of measures are obtained from the values of the fractal dimension at different scales. Therefore such values are used as inputs for a state-of-the-art classifier, the Support Vector Machine, which finally discriminates the images according to the respective species.Comment: 7 pages, 5 figure

Automatically measuring early and late leaf spot lesions in peanut plants using digital image processing.

Abstract. This paper presents a method to measure the lesions originated by the Cercospora arachidicola and Cercosporidium personatum fungi, which cause, respectively, the early and late leaf spots in peanut plants. The proposed method is based on a modified version of a previous proposal by the author, and uses mainly well-known image processing techniques, as well as specialist knowledge, to separate lesions from healthy tissue. The resulting tool provides good area estimates with minimum user interference and low computational burden.SBIAgro 2013

Banana Leaf Disease Identification Technique

There is no machine learning techniques have been used in an attempt to detect diseases in the banana plant such as banana bacterial wilt (BBW) and banana black sigatoka (BBS) that have caused a huge loss to many banana growers. The study investigated various computer vision techniques which led to the development of an approach that consists of four main phases. In phase one, images of Banana leaves were acquired using a standard digital camera. Phase two is the preprocessing phase where resizing and morphological operations occur. Next phase is the segmentation phase which translates RGB(Red Green Blue) image to YCbCr (Luminance Chrominance) color space which is then converted to a gray scale image and finally to a binarized image using Adaptive Contrast Map method. Next is the feature extraction phase where extraction of leaf features like color, texture and, shape occurs. Then comes the prominent phase were classification done Using Support Vector Machine classifier as classifier. Lastly, the performance of the classifier is evaluated to determine whether a leaf is diseased or not

Growth and Production Performance of Sugar Cane (Saccharum officinarum L.) Clon SB 01, SB 04, sb 19, SB 20 in the Village Curahmalang, Jombang Regency

Sugar productivity decreased in 2015-2016 by 11.76% and reached the lowest productivity in 2017 of 4,985 kg/ha White Crystal Sugar (GKP). The reason could be due to the presence of several types of sugarcane that experienced a decrease in yield during cultivation, such as nutrient deficiencies which resulted in divergent sugarcane plant height. Then the low sugarcane juice contained in sugarcane plants also affected production results. This study aimed to determine the growth and yield performance of sugarcane (Saccharum officinarum L.) clones SB01, SB04, SB19, SB20. This research was conducted in the garden of the Center for Sugarcane Research and Development (P3T) Faculty of Agriculture, University of Muhammadiyah Gresik, PG GEMPOLKREP and PT Perkebunan Nusantara X (PTPN X). Sumobito District, Jombang Regency from March to June 2021. Materials for observations used clones SB 01, clones SB 04, clones SB 19, and clones SB 20. The  variables observed were sugarcane shoots, sugar cane stalks, and sugarcane leaves. Data analysis used descriptive analytical, variability and heritability