Machine Learning Based Plant Illness Identification System Bolstering Agricultural Productivity Using Texture Analysis

Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning

Chili is a type of vegetable that has a very high economic value. The problem that often occurs in chili plants is that many agricultural losses are caused by disease. Plant diseases are always considered a very serious problem in all countries because economic growth is largely dependent on the agricultural sector in developing countries. In some plant, diseases sometimes caused by bacteria, viruses and fungi. To anticipate this problem, a method designed into a classification system for diagnosing chili leaf disease by applying the Gray Level Cooccurrence Matrix (GLCM) feature extraction method. Then classified using the Support Vector Machine (SVM) method. The output classification of disease diagnoses in chili obtained an overall accuracy level of 88%. The results obtained prove that the method of extracting the features of Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine (SVM) can be applied to diagnosing chili plants disease.

Several pests feed on leaves, stems, bases, and the entire plant, causing plant illnesses. As a result, it is vital to identify and eliminate the disease before causing any damage to plants. Manually detecting plant disease and treating it is pretty challenging in this period. Image processing is employed to detect plant disease since it requires much effort and an extended processing period. The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases, including <i>Phytophthora infestans</i>, Fusarium graminearum, Puccinia graminis, tomato yellow leaf curl. Therefore, this work uses the Support vector machine (SVM) classifier to detect and classify the plant disease using various steps like image acquisition, Pre-processing, Segmentation, feature extraction, and classification. The gray level co-occurrence matrix (GLCM) and the local binary pattern features (LBP) are used to identify the disease-affected portion of the plant leaf. According to experimental data, the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy.

Plant disease is main reason of agricultural crops production losses. Leaf disease in plant occurs due to fungai, virus and bacterias. Image contains various important features which is used in classification. In this paper author initially detect disease then classify disease using extracted features. It takes five diseased leaves (Black rot, Black Measles, Leaf blight, Septoria leaf spot, Bacterial spot) and healthy leaf images then identify leaf is diseased or healthy then if leaf is diseased then classify type of disease. Color features mean, standard deviation, skewness and kurtosis computed then Region based shape feature calculated to identify the size of spots. Texture feature calculated using gray level co-occurrence matrix (GLCM) which identifies texture of image using distance and 45° angle variation in GLCM. Extracted features sent to trained feed forward neural network and classify diseased with color, shape and texture feature individually and combination of all features then observe that combination of color, shape and texture feature improve the performance of classification accuracy.

Tomato cultivation is increasingly widespread, yet it faces significant challenges, particularly from plant diseases caused by fungi, bacteria, and insects. Addressing these diseases is crucial for ensuring the quality and yield of tomato crops. To support specialists in accurately identifying and managing these diseases, we propose an advanced automatic system for detecting and identifying tomato leaf diseases using sophisticated image processing techniques. Therefore, we proposed an approach that employs robust feature extraction methods, including the gray level co‐occurrence matrix (GLCM) and scale‐invariant feature transform (SIFT), coupled with a support vector machine (SVM) for adequate classification. We curated an extensive dataset of 2700 tomato leaf images, with a minimum of 300 images for each of the nine distinct disease classes. This comprehensive dataset facilitated the training and testing of various machine learning and deep learning models. The experimental results highlight our proposed approach’s exceptional accuracy and reliability, significantly improving the detection and classification of tomato leaf diseases. A thorough comparative analysis with contemporary state‐of‐the‐art techniques further validates the superiority of our system. Our findings suggest that this framework can significantly benefit tomato cultivation by enabling timely and precise disease management. Future research can explore integrating advanced deep learning algorithms to enhance the system’s accuracy in this multiclass classification challenge.

Can magnetic resonance imaging texture analysis change the breast imaging reporting and data system category of breast lesions?

본 논문에서는 비균일 양자화에 기반을 둔 영상의 질감분석에 널리 이용되고 있는 gray level co-occurrence matrix(GLCM)의 성능개선을 제안하였다. 여기서 비균일 양자화는 평균자승오차의 최소화를 위한 반복계산 기법인 Lloyd 알고리즘을 이용하였다. 이는 영상에서의 비균일 양자화 과정으로 얻어지는 비선형의 명암레벨을 GLCM의 생성에 이용함으로써 행렬의 차원을 감소시켜, GLCM의 생성과 질감특성 파라미터들의 계산에 따른 부하를 줄이기 위함이다. 제안된 기법을 30개의 <TEX>$120{\times}120$</TEX> 픽셀의 256 그레이 레벨을 가진 영상들을 대상으로 적용하여 angular second moment, contrast, variance, entropy, correlation, inverse difference moment 6개의 질감특성 파라미터들을 각각 계산한 실험결과, 양자화를 수행하지 않은 256 레벨 GLCM에 비해 계산시간과 저장 공간에서 개선된 성능이 있음을 확인하였다. 특히 48, 32, 16, 12, 8의 비균일 양자화 레벨 중에서 16일 때 가장 우수한 질감특성분석 성능이 있음을 알 수 있었다. This paper presents a performance improvement of gray level co-occurrence matrix(GLCM) based on the nonuniform quantization, which is generally used to analyze the texture of images. The nonuniform quantization is given by Lloyd algorithm of recursive technique by minimizing the mean square error. The nonlinear intensity levels by performing nonuniformly the quantization of image have been used to decrease the dimension of GLCM, that is applied to reduce the computation loads as a results of generating the GLCM and calculating the texture parameters by using GLCM. The proposed method has been applied to 30 images of <TEX>$120{\times}120$</TEX> pixels with 256-gray level for analyzing the texture by calculating the 6 parameters, such as angular second moment, contrast, variance, entropy, correlation, inverse difference moment. The experimental results show that the proposed method has a superior computation time and memory to the conventional 256-level GLCM method without performing the quantization. Especially, 16-gray level by using the nonuniform quantization has the superior performance for analyzing textures to another levels of 48, 32, 12, and 8 levels.

The detection of disease in clove plant leaves is generally carried out by diagnosing the symptoms that appear on clove plants. This diagnosis is conducted by clove farmers only by relying on their experience or even having to seek information from other clove farmers. This is because the agricultural sector has no disease detection system for clove leaves by utilizing digital image processing technology to detect diseases in clove leaves. In this study, the researchers applied two methods to make it easier for clove farmers to diagnose diseases in their clove plants. Those methods were the imaging system using Gray Level Co-Occurrence Matrix (GLCM) and disease clustering using the K-Means algorithm. The objective of this study was to design and build image pattern recognition by utilizing 4 features of the GLCM: energy, entropy, homogeneity, and contrast. These 4 features were used to obtain the extraction value from an image. The outcomes were then used to cluster the clove plant diseases using the K-Means method. In making the software, the researchers used Javascript, HTML, CSS, PHP, and MySql to create a database. The output in this study was an information system application that provides disease-type clustering using the K-Means algorithm. The results of the GLCM concerning extracting images of clove plant leaves affected by disease indicated that the created system can be used to help clove farmers in diagnosing what diseases are infecting their plants by only uploading photos from affected leaves of the clove plant. Furthermore, the results of the K-Means calculation on the examined data showed several categories of Anthracnose leaf spot diseases. In addition, sample number #40 was included in cluster 2 status, in which the average values for energy, entropy, homogeneity, and contrast were 0.583, 0.175, 0.939, and 0.175, respectively.

In this article, local monotonic pattern (LMP) paired with grey level co‐occurrence matrix (GLCM) methods are suggested to identify facial emotions with a high identification rate even when the face pictures are rotated. The proposed method extracts image features using the properties of the LMP algorithm and features extracted from the GLCM, which are then fed into the Support Vector Machine (SVM) process that reduces the dimensionality of the features vector and classifies the output into different facial expressions or emotions. The SVM performance rate is then compared to the K‐nearest neighbour approach (KNN) to see which method produces the best facial emotion identification and categorization. The study identified facial emotions in the images using advanced algorithms of GLCM and LMP models to be compared. As a result, the accuracy of SVM and KNN was utilized to determine the method's usefulness in classification using the application of MATLAB. A result of more than 93% was achieved using the SVM method compared with 89.4% using the KNN for the recognition process. The study also demonstrated that this approach would lead to more classification outcomes if the LMP and GLCM are combined with an edge‐based technique yielding a new method that is more efficient and more effective.

Diseases and pests in plants/crops are major causes of significant agricultural losses with economic, social and ecological impacts. Therefore, there is a need for early identification of plant diseases and pests through automated systems. Recently, machine learning-based methods have become popular in solving agricultural problems such as plant diseases faced by technically-noob farmers. This work proposes a novel method based on stacking ensemble machine learning to detect plant diseases in Uradbean precisely. Two classifiers: support vector machine (SVM), random forest (RF) are trained on a dataset consists of Uradbean infected and healthy leaf images. These classifiers are stacked with logistic regression (LR) classifier. In the diverse ensemble, LR classifier is used as a meta-learner which enhanced the precision of the disease classification. The fuzzy C-Means clustering with particle swarm optimization is used for image segmentation. Haralick, Hu Moments and color histogram methods are used in feature extraction. During the tests, the proposed model is also compared with pre-trained networks: DenseNet-201, ResNet-50, and VGG19. It achieved an impressive classification accuracy of 96.82 % which is higher than the individual classifiers and pre-trained networks. To validate model performance, it is evaluated on a benchmark public dataset consists of Apple leaf images and achieved 98.30% accuracy. It is observed that ensemble method reflects an advantage over individual models in increasing the classification rates and reducing the computational overhead in comparison to pre-trained networks which struggle due to the issues such as irrelevant features, generation of pertinent characteristics, and noise

The agricultural sector worldwide faces significant challenges from plant diseases that threaten crop yields, food security, and economic stability. This study introduces "PlantOHealth," a detailed comparative analysis of five models for detecting plant diseases using leaf images, leveraging advancements in deep learning. The models evaluated include a basic Convolutional Neural Network (CNN) and four transfer learning frameworks: VGG16, VGG19, MobileNetV2, and ResNet, all utilizing the PlantVillage dataset with balanced classes achieved through oversampling and undersampling techniques. MobileNetV2 emerged as the most effective, achieving an accuracy of 99.40% while maintaining computational efficiency for resource- constrained environments, followed by the CNN with an accuracy of 98.68%. VGG19 and VGG16 attained accuracies of 98.92% and 97.24%, respectively, while ResNet recorded the lowest at 96.24%. Graphical analyses provided deep insights into model performance and highlighted the trade-offs between accuracy and computational demands. "PlantOHealth" contributes to the integration of AI in agriculture, offering actionable insights for researchers and practitioners, while future work will focus on exploring advanced techniques like ensemble learning to enhance plant disease detection systems further, ultimately supporting sustainable agricultural practices and improving food security.

Problem statement: Recently, there has been a huge progress in collection of varied image databases in the form of digital. Most of the users found it difficult to search and retrieve required images in large collections. In order to provide an effective and efficient search engine tool, the system has been implemented. In image retrieval system, there is no methodologies have been considered directly to retrieve the images from databases. Instead of that, various visual features that have been considered indirect to retrieve the images from databases. In this system, one of the visual features such as texture that has been considered indirectly into images to extract the feature of the image. That featured images only have been considered for the retrieval process in order to retrieve exact desired images from the databases. Approach: The aim of this study is to construct an efficient image retrieval tool namely, “Content Based Medical Image Retrieval with Texture Content using Gray Level Co-occurrence Matrix (GLCM) and k-Means Clustering algorithms”. This image retrieval tool is capable of retrieving images based on the texture feature of the image and it takes into account the Pre-processing, feature extraction, Classification and retrieval steps in order to construct an efficient retrieval tool. The main feature of this tool is used of GLCM of the extracting texture pattern of the image and k-means clustering algorithm for image classification in order to improve retrieval efficiency. The proposed image retrieval system consists of three stages i.e., segmentation, texture feature extraction and clustering process. In the segmentation process, preprocessing step to segment the image into blocks is carried out. A reduction in an image region to be processed is carried out in the texture feature extraction process and finally, the extracted image is clustered using the k-means algorithm. The proposed system is employed for domain specific based search engine for medical Images such as CT-Scan, MRI-Scan and X-Ray. Results: For retrieval efficiency calculation, conventional measures namely precision and recall were calculated using 1000 real time medical images (100 in each category) from the MATLAB Workspace database. For selected query images from the MATLAB-Image Processing tool Box-Workspace Database, the proposed tool was tested and the precision and recall results were presented. The result indicates that the tool gives better performance in terms of percentage for all the 1000 real time medical images from which the scalable performance of the system has been proved. Conclusion: This study proposed a model for the Content Based Medical Image Retrieval System by using texture feature in calculating the Gray Level Co Occurrence matrix (GLCM) from which various statistical measures were computed in order to increasing similarities between query image and database images for improving the retrieval performance along with the large scalability of the databases.

Texture analysis is a fundamental approach in image processing for identifying specific patterns or structures. One widely used method is the grey-level co-occurrence matrix (GLCM), which computes the frequency of pixel value pairs at certain distances and angles. This study adapts the GLCM method for 1D electroencephalogram (EEG) signal analysis, focusing on extracting features such as contrast, energy, homogeneity, correlation, and entropy. EEG signals are normalized to the range 0–255, and the extracted features are classified using a support vector machine (SVM). Experimental results show that combining features across multiple distances (d=1 to 20) achieves classification accuracy of 78.8% for five classes (Z/O/N/F/S), 94.0% for three classes (O/F/S), and 94.3% for another three-class group (Z/N/F). The method shows strong performance for short to medium distances and fewer class combinations. However, performance declines when dealing with more complex class sets and longer distances, where texture features become less effective. The drop in accuracy for Z/O/N/F/S beyond d=5 underscores the challenges of maintaining feature robustness at extended distances. Despite this, GLCM remains a promising approach for EEG signal classification. Future work should focus on optimizing distance parameters and feature combinations to further enhance classification performance.

Plant disease is known as the largest intimidation, which directly affects on production and causes economic loss. Traditionally, human experience is used in defining the class of plant disease, but this often leads to wrong recognition. Thanks to the rapid development of modern technology, many machine-based methods with high efficiency and accuracy can be easily developed. Recently, computer vision has been widely used in various applications which have become intelligent supporters of human. This paper proposes an image-based method using Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) feature extraction for plant disease recognition. Contrast enhancement is used to increase the visual quality in the pre-processing stage. Finally, the classification is done by Support Vector Machine (SVM), and different schemes are used to justify the performance. Overall, the obtained result demonstrates that the proposed approach can identify six classes of plant-based on leaves.

Performance evaluation of GLCM and pixel intensity matrix for skin texture analysis