Plant Disease Detection in Image Processing Using MATLAB

For increasing growth and productivity of crop field, farmers need automatic monitoring of disease of plants instead of manual. Manual monitoring of disease do not give satisfactory result as naked eye observation is old method requires more time for disease recognition also need expert hence it is non effective. So in this paper, we introduced a modern technique to find out disease related to both leaf and fruit. To overcome disadvantages of traditional eye observing technique, we used digital image processing technique for fast and accurate disease detection of plant. In our proposed work, we developed k-means clustering algorithm with multi SVM algorithm in MATLAB software for disease identification and classification

SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique

Computers have been used for mechanization and automation in different applications of agriculture/horticulture. The critical decision on the agricultural yield and plant protection is done with the development of expert system (decision support system) using computer vision techniques. One of the areas considered in the present work is the processing of images of plant diseases affecting agriculture/horticulture crops. The first symptoms of plant disease have to be correctly detected, identified, and quantified in the initial stages. The color and texture features have been used in order to work with the sample images of plant diseases. Algorithms for extraction of color and texture features have been developed, which are in turn used to train support vector machine (SVM) and artificial neural network (ANN) classifiers. The study has presented a reduced feature set based approach for recognition and classification of images of plant diseases. The results reveal that SVM classifier is more suitable for identification and classification of plant diseases affecting agriculture/horticulture crops

Deep CNN and MLP-based vision systems for algae detection in automatic inspection of underwater pipelines

Artificial neural networks, such as the multilayer perceptron (MLP), have been increasingly employed in various applications. Recently, deep neural networks, specially convolutional neural networks (CNN), have received considerable attention due to their ability to extract and represent high-level abstractions in data sets. This work describes a vision inspection system based on deep learning and computer vision algorithms for detection of algae in underwater pipelines. The proposed algorithm comprises a CNN or a MLP network, followed by a post-processing stage operating in spatial and temporal domains, employing clustering of neighboring detection positions and a region interception framebuffer. The performances of MLP, employing different descriptors, and CNN classifiers are compared in real-world scenarios. It is shown that the post-processing stage considerably decreases the number of false positives, resulting in an accuracy rate of 99.39%.Redes neurais artificiais, como o perceptron multicamada (MLP), têm sido cada vez mais empregadas em várias aplicações. Recentemente, as redes neurais profundas (deep neural networks), especialmente as redes neurais convolutivas (CNN), receberam atenção considerável devido à sua capacidade de extrair e representar abstrações de alto nível em conjuntos de dados. Esta dissertação descreve um sistema de inspeção automático baseado em algoritmos de aprendizado profundo (deep learning) e visão computacional para detecção de algas em dutos submarinos. O algoritmo proposto compreende uma rede CNN ou MLP, seguida de uma fase de pós-processamento que opera em domínios espaciais e temporais, empregando agrupamento de posições de detecção vizinhas e um buffer das regiões de interseção ao longo dos quadros. Os desempenhos de MLP, empregando diferentes descritores, e os classificadores CNN são comparados em cenários do mundo real. Mostra-se que a fase de pos-processamento diminui consideravelmente o número de falsos positivos, resultando em uma taxa de acerto de 99,39%

SOYBEAN LEAF DISEASES DETECTION AND CLASSIFICATION USING RECENT IMAGE PROCESSING TECHNIQUES

Purpose: India is an agricultural country and soybean production is one of the major sources of earning. Due to the major factors like diseases, pest attacks, and sudden changes in the weather condition, the productivity of the soybean crop decreases. Automatic detection of soybean plant diseases is essential to detect the symptoms of soybean diseases as early as they appear on the growing stage. This paper proposed a methodology for the analysis and detection of soybean plant leaf diseases using recent digital image processing techniques. In this paper, experimental results demonstrate that the proposed method can successfully detect and classify the major soybean diseases. Methodology: MatLab 18a is used for the simulation for the result and machine learning-based recent image processing techniques for the detection of the soybean leaf disease. Main Findings: The main finding of this work is to create the soybean leaf database which includes healthy and unhealthy leaves and achieved 96 percent accuracy in this work using the proposed methodology. Applications of this study: To detect soybean plant leaf diseases in the early stage in Agricultural. The novelty of this study: Self-prepared database of healthy and unhealthy images of soybean leaf with the proposed algorithm

A Review of using Data Mining Techniques in Power Plants

Data mining techniques and their applications have developed rapidly during the last two decades. This paper reviews application of data mining techniques in power systems, specially in power plants, through a survey of literature between the year 2000 and 2015. Keyword indices, articles’ abstracts and conclusions were used to classify more than 86 articles about application of data mining in power plants, from many academic journals and research centers. Because this paper concerns about application of data mining in power plants; the paper started by providing a brief introduction about data mining and power systems to give the reader better vision about these two different disciplines. This paper presents a comprehensive survey of the collected articles and classifies them according to three categories: the used techniques, the problem and the application area. From this review we found that data mining techniques (classification, regression, clustering and association rules) could be used to solve many types of problems in power plants, like predicting the amount of generated power, failure prediction, failure diagnosis, failure detection and many others. Also there is no standard technique that could be used for a specific problem. Application of data mining in power plants is a rich research area and still needs more exploration

Deep Learning and Computer Vision based Model for Detection of Diseased Mango Leaves

Mangifera Indica, commonly known as mangoes, is the most commercialized export fruit crop in India, accounting for about 40% of the total global production. Due to its widespread production, it is vulnerable to a variety of diseases that affect its yield and resulting in loss. These diseases like Anthracnose, Powdery Mildew, Leaf blights, etc., occur primarily on leaves. As a result, there is a great need for a system that helps in the detection of diseased mango leaves. In this paper, we propose a system that makes use of pre-trained Convolutional Neural Network architecture, the ResNet-50 for the detection of infected mango leaves. The dataset contains 435 images of mango leaves with binary classification as healthy and diseased. These images are pre-processed by resizing them and applying CLAHE. After applying in-place data augmentation on the dataset, the features are extracted using the ResNet-50 model. For the classification process, we make use of fine-tuned head and Machine Learning classifiers such as Support Vector Machine, Gradient Boosting, Logistic Regression, XGBoost, Decision Tree, and K Nearest Neighbour. Among them, the fine-tuned head classifier achieved an accuracy of 97.7%, and Machine Learning classifiers such as SVM, Logistic Regression obtained an accuracy of 100%. The experimental results obtained validate that the system is efficient in its performance of detecting the two classes of mango leaves

Image Analysis using Color Co-occurrence Matrix Textural Features for Predicting Nitrogen Content in Spinach

This study aimed to determine the nitrogen content of spinach leaves by using computer imaging technology. The application of Color Co-occurrence Matrix (CCM) texture analysis was used to recognize the pattern of nitrogen content in spinach leaves. The texture analysis consisted of 40 CCM textural features constructed from RGB and grey colors. From the 40 textural features, the best features-subset was selected by using features selection method. Features selection method can increase the accuracy of image analysis using ANN model to predict nitrogen content of spinach leaves. The combination of ANN with Ant Colony Optimization resulted in the most optimal modelling with mean square error validation value of 0.0000083 and the R2 testing-set data = 0.99 by using 10 CCM textural features as the input of ANN. The computer vision method using ANN model which has been developed can be used as non-invasive sensing device to predict nitrogen content of spinach and for guiding farmers in the accurate application of their nitrogen fertilization strategies using low cost computer imaging technology