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Radial basis function neural network and salp swarm algorithm for paddy leaf diseases classification in Thanjavur, Tamilnadu geographical region

Thirugnanasambandam Gayathri Devi, Rajkumar Ganesan, Srinivasan Anandan, Sudha Selvarajan

Acta Geophysica

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2022

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Vol. 70, no 6

2917--2932

EN

Farming is an essential sustenance for the progressive population. The development of our country depends on the farmers. Plants endure by many diseases due to environmental factors. So, the farmers need to detect plant diseases at an early stage for appreciable yield. In the beginning, the observing and examining plant disease are examined physically by the expertise in the farming field, which requires a considerable measure of work/ and requires over the top handling time. Now, machine learning concepts eliminate conventional protruding and time-consuming techniques. This paper focuses on a novel method for detecting and identifying paddy leaf diseases at the early stages in Thanjavur region using radial basis function neural network (RBFNN) classifier. Further, it is optimized with salp swarm algorithm (SSA) technique. The proposed method utilizes the data from the TNAU agritech portal, IRRI knowledge bank, UCI machine learning repository databases, which have healthy and diseased images. This work illustrates four categories (Bacterial Blast, Bacterial Blight, Leaf Tungro and Brown Spot) of infected paddy images along with the normal set of images. Initially the preprocessing is performed for the acquired images then K-means segmentation algorithm segregates the image. Gray level co-occurrence matrix extracts the Texture features from the segmented image and the RBFNN classifier performs the disease classification and improves the detection accuracy by optimizing the data using SSA. The investigational results of the proposed methodology exhibit the performance in terms of accuracy of disease detection is 98.47%. However, radial basis function neural network (RBFNN) achieves the diseases detection accuracy of 97.85% and support-vector machine (SVM) classifier achieves a disease detection accuracy of 97.07%. This paper proposes a method of paddy leaf disease recognition and classification using RBFNN and salp swarm algorithm. It also suggests and identifies an image analysis by framing a set of conditions for disease affected plants. The results show that the most satisfactory outcome can be gained to verify the yield of proposed methods with least effort.

2

Measurement of the Uniformity of Thermally Bonded Points in Polypropylene Spunbonded Non-Wovens Using Image Processing and its Relationship With Their Tensile Properties

Emadi M., Tavanaie M. A., Payvandy P.

Autex Research Journal

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2018

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Vol. 18, no. 4

405--418

EN

This article aims at the image processing of surface uniformity and thermally bonded points uniformity in polypropylene spunbonded non-wovens. The investigated samples were at two different weights and three levels of non-uniformity. An image processing method based on the k-means clustering algorithm was applied to produce clustered images. The best clustering procedure was selected by using the lowest Davies-Bouldin index. The peak signal-to-noise ratio (PSNR) image quality evaluation method was used to choose the best binary image. Then, the non-woven surface uniformity was calculated using the quadrant method. The uniformity of thermally bonded points was calculated through an image processing method based on morphological operators. The relationships between the numerical outcomes and the empirical results of tensile tests were investigated. The results of image processing and tensile behavior showed that the surface uniformity and the uniformity of thermally bonded points have great impacts on tensile properties at the selected weights and non-uniformity levels. Thus, a sample with a higher level of uniformity and, consequently, more regular bonding points with further bonding percentage depicts the best tensile properties.

3

Localization of component lead inside a THT solder joint for solder defects classification

Fonseka C. L. S. C., Jayasinghe J. A. K. S.

Journal of Achievements in Materials and Manufacturing Engineering

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2017

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Vol. 83, nr 2

57--66

EN

Purpose: Automatic Optical Inspection (AOI) systems, used in electronics industry have been primarily developed to inspect soldering defects of Surface Mount Devices (SMD) on a Printed Circuit Board (PCB). However, no commercially available AOI system exists that can be integrated to a desktop soldering robotic system, which is capable of identifying soldering defects of Through Hole Technology (THT) solder joints along with the soldering process. In our research, we have implemented an AOI platform that is capable of performing automatic quality assurance of THT solder joints in a much efficient way. In this paper, we have presented a novel approach to identify soldering defects of THT solder joints, based on the location of THT component lead top. This paper presents the methodologies that can be used to precisely identify and localize THT component lead inside a solder joint. Design/methodology/approach: We have discussed the importance of lead top localization and presented a detailed description on the methodologies that can be used to precisely segment and localize THT lead top inside the solder joint. Findings: It could be observed that the precise localization of THT lead top makes the soldering quality assurance process more accurate. A combination of template matching algorithms and colour model transformation provide the most accurate outcome in localizing the component lead top inside solder joint, according to the analysis carried out in this paper. Research limitations/implications: When the component lead top is fully covered by the soldering, the implemented methodologies will not be able to identify the actual location of it. In such a case, if the segmented and detected lead top locations are different, a decision is made based on the direction in which the solder iron tip touches the solder pad. Practical implications: The methodologies presented in this paper can be effectively used to have a precise localization of component lead top inside the solder joint. The precise identification of component lead top leads to have a very precise quality assurance capability to the implemented AOI system. Originality/value: This research proposes a novel approach to identify soldering defects of THT solder joints in a much efficient way based on the component lead top. The value of this paper is quite high, since we have taken all the possibilities that may appear on a solder joint in a practical environment.

4

K-means clustering in textured image: example of lamellar microstructure in titanium alloys

Al Darwich R., Babout L., Strzecha K.

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2017

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T. 7, nr 3

43--46

EN

This paper presents an implementation of the k-means clustering method, to segment cross sections of X-ray micro tomographic images of lamellar Titanium alloys. It proposes an approach for estimating the optimal number of clusters by analyzing the histogram of the local orientation map of the image and the choice of the cluster centroids used to initialize k-means. This is compared with the classical method considering random coordinates of the clusters.

PL

W artykule przedstawiono implementację metody klasteryzacji k-średnich, do segmentacji dwuwymiarowych rentgenowskich obrazów mikro tomograficznych lamelarnych stopów tytanu. Zaproponowano metody szacowania optymalnej liczbę klastrów oraz wyboru centro idów poprzez analizę histogramu mapy lokalnych kierunków obrazu. Dokonano porównania zaproponowanych metod z losowym doborem początkowego położenia klastrów.

5

Next day electric load curve forecasting using k-means clustering

Dudek G.

Rynek Energii

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2011

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nr 1

143-149

EN

Accurate models for electric power load forecasting are essential to the operation and planning for the electric industry. They have many applications including energy purchasing, generation, distribution, and contract evaluation. This paper proposes the methods of short-term load forecasting using the k-means clustering. Two approaches are presented based on the similarity of the load sequence patterns. In the first one, each cluster is created from two preprocessed sequences of load time series: one preceding the forecast moment and the forecasted one. In the forecast procedure only the first part is presented to the model. The second forecasted part is reconstructed from the cluster closest to the first part. In the second approach both sequences are divided into clusters independently. After clustering the empirical probabilities that the forecasted sequence is associated to cluster j when the corresponding input sequence is associated to cluster i are calculated. The forecasted sequence for the new input sequence is formed from cluster centroids using these conditional probabilities. The suitability of the proposed approaches is illustrated through an application to real load data.

PL

W tym artykule proponuje się metody prognozowania krótkoterminowego oparte na klasteryzacji k-średnich. Zaprezentowano dwa podejścia wykorzystujące podobieństwo obrazów sekwencji szeregu czasowego obciążeń. W pierwszym podejściu, każdy klaster tworzony jest z dwóch przetworzonych sekwencji szeregu czasowego obciążeń: poprzedzającej moment prognozy i prognozowanej. W procedurze prognostycznej tylko pierwsza sekwencja jest prezentowana na wejście modelu. Druga sekwencja, prognozowana, rekonstruowana jest z klastera najbliższego do sekwencji pierwszej. W drugim podejściu obie sekwencje dzielone są na grupy niezależnie. Po fazie grupowania wyznacza się empiryczne prawdopodobieństwa, że prognozowana sekwencja należy do grupy j, pod warunkiem, że odpowiadająca jej sekwencja poprzedzająca należy do grupy i. Sekwencja prognozowana dla sekwencji wejściowej formowana jest z centroidów klasterów, przy użyciu tych warunkowych prawdopodobieństw. Skuteczność proponowanych metod zilustrowano przykładami prognoz wykonanych na rzeczywistych danych.

6

A Probabilistic Component for K-Means Algorithm and its Application to Sound Recognition

Olszewski D., Kołodziej M., Twardy M.

Przegląd Elektrotechniczny

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2010

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R. 86, nr 6

185-190

EN

In this paper, we present a novel approach to building of a probabilistic model of the data set, which is further used by the K-means clustering algorithm. Considering K-means with respect to the probabilistic model, requires incorporating of a probabilistic distance, which provides us with measure of similarity between two probability distributions, as the distance measure. We use various kinds of probabilistic distances in order to evaluate their effectiveness when applied to the algorithm with the proposed model of the analyzed data. Further, we report the results of experiments with the discussed clustering algorithm in the field of sound recognition and choose these probabilistic distances, which correspond to the highest clustering performance. As a reference technique, we used the traditional K-means algorithm with the most commonly employed Euclidean distance. Our experiments have shown that the presented method outperforms the traditional K-means algorithm, regardless of the statistical distance applied.

PL

W niniejszej pracy zaprezentowano nowy sposób budowy probabilistycznego modelu zbioru danych, analizowanych przez algorytm klasteryzacji K-średnich. Rozważanie metody K-średnich w odniesieniu do modelu probabilistycznego, narzuca wymaganie wykorzystania odległości probabilistycznej, będącej miarą podobieństwa pomiędzy dwoma rozkładami prawdopodobieństwa, jako miary odległości w algorytmie. W pracy wykorzystano różne typy odległości probabilistycznych, w celu oceny skuteczności ich zastosowania w algorytmie z proponowanym modelem analizowanych danych. Przedstawione zostały również wyniki badań omawianego algorytmu w dziedzinie rozpoznawania dźwięku. Jako punkt odniesienia wykorzystany został tradycyjny algorytm K-średnich z najczęściej stosowaną odległością Euklidesa. Wyniki przeprowadzonych badań pozwalają stwierdzić, iż zaprezentowana metoda umożliwia osiągnięcie lepszych rezultatów klasteryzacji niż klasyczny algorytm K-średnich, w przypadku każdej zastosowanej odległości statystycznej.