Leaf Disease Detection Using AI

2021 ◽  
pp. 110-136
Author(s):  
Praveen Kumar Maduri ◽  
Tushar Biswas ◽  
Preeti Dhiman ◽  
Apurva Soni ◽  
Kushagra Singh
Keyword(s):  
Machine Learning ◽  
Clustering Algorithms ◽  
Essential Elements ◽  
Plant Diseases ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Initial Stage ◽  
Large Farm ◽  
Time And Energy ◽  
Financial Losses

Plants play a significant role in everyone's life. They provide us essential elements like food, oxygen, and shelter, so plants must be supervised and nurtured properly. During cultivation, crops are prone to different kinds of diseases which can severely damage the whole yield leading to financial losses for farmers. In last 10 years, researchers have used different machine learning techniques to detect the disease on plants, but either the methods were not efficient enough to be implemented or were not able to cover the wide area in which plant diseases can be detected. So, the author has introduced a method which is efficient enough to easily detect plant disease and can be implemented in large fields. The author has used a combination of CNN and k-means clustering algorithms. By using this method, crops disease is detected by analyzing the leaves, which notifies users for action in the initial stage. Thus, the proposed method prevents whole crops from getting damaged and saves time and energy of farmers as disease will be identified way before a human eye can detect it on a large farm.

Selecting optimal SpMV realizations for GPUs via machine learning

2021 ◽  
pp. 109434202199073
Author(s):  
Ernesto Dufrechou ◽  
Pablo Ezzatti ◽  
Enrique S Quintana-Ortí
Keyword(s):  
Machine Learning ◽  
Sparse Matrix ◽  
Machine Learning Techniques ◽  
Optimal Method ◽  
Learning Techniques ◽  
General Rules ◽  
Machine Learning Approach ◽  
The Matrix ◽  
Time And Energy ◽  
Matrix Vector

More than 10 years of research related to the development of efficient GPU routines for the sparse matrix-vector product (SpMV) have led to several realizations, each with its own strengths and weaknesses. In this work, we review some of the most relevant efforts on the subject, evaluate a few prominent routines that are publicly available using more than 3000 matrices from different applications, and apply machine learning techniques to anticipate which SpMV realization will perform best for each sparse matrix on a given parallel platform. Our numerical experiments confirm the methods offer such varied behaviors depending on the matrix structure that the identification of general rules to select the optimal method for a given matrix becomes extremely difficult, though some useful strategies (heuristics) can be defined. Using a machine learning approach, we show that it is possible to obtain unexpensive classifiers that predict the best method for a given sparse matrix with over 80% accuracy, demonstrating that this approach can deliver important reductions in both execution time and energy consumption.

Exploring multi-modalities in weather prediction using a univariate graph based on machine learning techniques

2021 ◽  
Author(s):  
Natacha Galmiche ◽  
Nello Blaser ◽  
Morten Brun ◽  
Helwig Hauser ◽  
Thomas Spengler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Standard Deviation ◽  
Probability Distributions ◽  
Weather Prediction ◽  
A Priori ◽  
Clustering Algorithms ◽  
Quantitative Information ◽  
Machine Learning Techniques ◽  
Topological Data Analysis ◽  
Learning Techniques

<p>Probability distributions based on ensemble forecasts are commonly used to assess uncertainty in weather prediction. However, interpreting these distributions is not trivial, especially in the case of multimodality with distinct likely outcomes. The conventional summary employs mean and standard deviation across ensemble members, which works well for unimodal, Gaussian-like distributions. In the case of multimodality this misleads, discarding crucial information. </p><p>We aim at combining previously developed clustering algorithms in machine learning and topological data analysis to extract useful information such as the number of clusters in an ensemble. Given the chaotic behaviour of the atmosphere, machine learning techniques can provide relevant results even if no, or very little, a priori information about the data is available. In addition, topological methods that analyse the shape of the data can make results explainable.</p><p>Given an ensemble of univariate time series, a graph is generated whose edges and vertices represent clusters of members, including additional information for each cluster such as the members belonging to them, their uncertainty, and their relevance according to the graph. In the case of multimodality, this approach provides relevant and quantitative information beyond the commonly used mean and standard deviation approach that helps to further characterise the predictability.</p>

scholarly journals Using Machine-Learning Techniques to Identify Responders vs. Non-responders in Randomized Clinical Trials.

2020 ◽  
Author(s):  
Vasiliki Nikolodimou ◽  
Paul Agapow
Keyword(s):  
Machine Learning ◽  
Randomized Clinical Trials ◽  
Clustering Algorithms ◽  
Human Monoclonal Antibody ◽  
Clinical History ◽  
Differential Response ◽  
Unsupervised Clustering ◽  
Machine Learning Techniques ◽  
Genetic Characteristics ◽  
Learning Techniques

Despite the expectation of heterogeneity in therapy outcomes, especially for complex diseases like cancer, analyzing differential response to experimental therapies in a randomized clinical trial (RCT) setting is typically done by dividing patients into responders and non-responders, usually based on a single endpoint. Given the existence of biological and patho-physiological differences among metastatic colorectal cancer (mCRC) patients, we hypothesized that a data-driven analysis of an RCT population outcomes can identify sub-types of patients founded on differential response to Panitumumab - a fully human monoclonal antibody directed against the epidermal growth factor receptor. Outcome and response data of the RCT population were mined with heuristic, distance-based and model-based unsupervised clustering algorithms. The population sub-groups obtained by the best performing clustering approach were then examined in terms of molecular and clinical characteristics. The utility of this characterization was compared against that of the sub-groups obtained by the conventional responders' analysis and then contrasted with aetiological evidence around mCRC heterogeneity and biological functioning. The Partition around Medoids clustering method results into the identification of seven sub-types of patients, statistically distinct from each other in survival outcomes, prognostic biomarkers and genetic characteristics. Conventional responders analysis was proven inferior in uncovering relationships between physical, clinical history, genetic attributes and differential treatment resistance mechanisms. Combined with improved characterization of the molecular subtypes of CRC, applying Machine Learning techniques, like unsupervised clustering, onto the wealth of data already collected by previous RCTs can support the design of further targeted, more efficient RCTs and better identification of patient groups who will respond to a given intervention.

scholarly journals Diagnosis of Diabetic Retinopathy Using Machine Learning Techniques

2021 ◽  
Author(s):  
Mehmet Akif Cifci
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
The Other ◽  
Machine Learning Techniques ◽  
Image Processing Techniques ◽  
Learning Techniques ◽  
Fundus Photographs ◽  
Processing Techniques ◽  
Time And Energy

The complication of people with diabetes causes an illness known as Diabetic Retinopathy (DR). It is very widespread among middle-aged and older people. As diabetes progresses, patients' vision may deteriorate and cause DR. People to lose their vision because of this illness. To cope with DR, early detection is needed. Patients will have to be checked by doctors regularly, which is a waste of time and energy. DR can be divided into two groups: non-proliferative (NPDR) while the other is proliferative (PDR). In this study, machine learning (ML) techniques are used to diagnose DR early. These are PNN, SVM, Bayesian Classification, and K-Means Clustering. These techniques will be evaluated and compared with each other to choose the best methodology. A total of 300 fundus photographs are processed for training and testing. The features are extracted from these raw images using image processing techniques. After an experiment, it is concluded that PNN has an accuracy of about 89%, Bayes Classifications 94%, SVM 97%, and K-Means Clustering 87%. The preliminary results prove that SVM is the best technique for early detection of DR.

scholarly journals Using Real-Time Data and Unsupervised Machine Learning Techniques to Study Large-Scale Spatio–Temporal Characteristics of Wastewater Discharges and their Influence on Surface Water Quality in the Yangtze River Basin

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1268 ◽  
Author(s):  
Zhenzhen Di ◽  
Miao Chang ◽  
Peikun Guo ◽  
Yang Li ◽  
Yin Chang
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
Real Time ◽  
Yangtze River Basin ◽  
Clustering Algorithms ◽  
Machine Learning Techniques ◽  
Unsupervised Machine Learning ◽  
Learning Techniques ◽  
The Yangtze River Basin ◽  
Spatio Temporal

Most worldwide industrial wastewater, including in China, is still directly discharged to aquatic environments without adequate treatment. Because of a lack of data and few methods, the relationships between pollutants discharged in wastewater and those in surface water have not been fully revealed and unsupervised machine learning techniques, such as clustering algorithms, have been neglected in related research fields. In this study, real-time monitoring data for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), pH, and dissolved oxygen in the wastewater discharged from 2213 factories and in the surface water at 18 monitoring sections (sites) in 7 administrative regions in the Yangtze River Basin from 2016 to 2017 were collected and analyzed by the partitioning around medoids (PAM) and expectation–maximization (EM) clustering algorithms, Welch t-test, Wilcoxon test, and Spearman correlation. The results showed that compared with the spatial cluster comprising unpolluted sites, the spatial cluster comprised heavily polluted sites where more wastewater was discharged had relatively high COD (>100 mg L−1) and NH3-N (>6 mg L−1) concentrations and relatively low pH (<6) from 15 industrial classes that respected the different discharge limits outlined in the pollutant discharge standards. The results also showed that the economic activities generating wastewater and the geographical distribution of the heavily polluted wastewater changed from 2016 to 2017, such that the concentration ranges of pollutants in discharges widened and the contributions from some emerging enterprises became more important. The correlations between the quality of the wastewater and the surface water strengthened as the whole-year data sets were reduced to the heavily polluted periods by the EM clustering and water quality evaluation. This study demonstrates how unsupervised machine learning algorithms play an objective and effective role in data mining real-time monitoring information and highlighting spatio–temporal relationships between pollutants in wastewater discharges and surface water to support scientific water resource management.

scholarly journals Analysis of Cattle Social Transitional Behaviour: Attraction and Repulsion

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5340
Author(s):  
Haocheng Xu ◽  
Shenghong Li ◽  
Caroline Lee ◽  
Wei Ni ◽  
David Abbott ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Interactions ◽  
Management Practices ◽  
Clustering Algorithms ◽  
Physical Distance ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Video Recordings ◽  
Learning Techniques ◽  
The Social

Understanding social interactions in livestock groups could improve management practices, but this can be difficult and time-consuming using traditional methods of live observations and video recordings. Sensor technologies and machine learning techniques could provide insight not previously possible. In this study, based on the animals’ location information acquired by a new cooperative wireless localisation system, unsupervised machine learning approaches were performed to identify the social structure of a small group of cattle yearlings (n=10) and the social behaviour of an individual. The paper first defined the affinity between an animal pair based on the ranks of their distance. Unsupervised clustering algorithms were then performed, including K-means clustering and agglomerative hierarchical clustering. In particular, K-means clustering was applied based on logical and physical distance. By comparing the clustering result based on logical distance and physical distance, the leader animals and the influence of an individual in a herd of cattle were identified, which provides valuable information for studying the behaviour of animal herds. Improvements in device robustness and replication of this work would confirm the practical application of this technology and analysis methodologies.

scholarly journals Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis

2020 ◽  
Vol 12 (15) ◽  
pp. 2421
Author(s):  
Kasra Rafiezadeh Shahi ◽  
Mahdi Khodadadzadeh ◽  
Laura Tusa ◽  
Pedram Ghamisi ◽  
Raimon Tolosana-Delgado ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Hyperspectral Image ◽  
Imaging Techniques ◽  
Clustering Algorithms ◽  
Machine Learning Techniques ◽  
Mixed Data ◽  
Number Of Clusters ◽  
Hyperspectral Image Analysis ◽  
Learning Techniques

Hyperspectral imaging techniques are becoming one of the most important tools to remotely acquire fine spectral information on different objects. However, hyperspectral images (HSIs) require dedicated processing for most applications. Therefore, several machine learning techniques were proposed in the last decades. Among the proposed machine learning techniques, unsupervised learning techniques have become popular as they do not need any prior knowledge. Specifically, sparse subspace-based clustering algorithms have drawn special attention to cluster the HSI into meaningful groups since such algorithms are able to handle high dimensional and highly mixed data, as is the case in real-world applications. Nonetheless, sparse subspace-based clustering algorithms usually tend to demand high computational power and can be time-consuming. In addition, the number of clusters is usually predefined. In this paper, we propose a new hierarchical sparse subspace-based clustering algorithm (HESSC), which handles the aforementioned problems in a robust and fast manner and estimates the number of clusters automatically. In the experiment, HESSC is applied to three real drill-core samples and one well-known rural benchmark (i.e., Trento) HSI datasets. In order to evaluate the performance of HESSC, the performance of the new proposed algorithm is quantitatively and qualitatively compared to the state-of-the-art sparse subspace-based algorithms. In addition, in order to have a comparison with conventional clustering algorithms, HESSC’s performance is compared with K-means and FCM. The obtained clustering results demonstrate that HESSC performs well when clustering HSIs compared to the other applied clustering algorithms.

scholarly journals Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques

2021 ◽  
Vol 10 (18) ◽  
pp. 4281
Author(s):  
Gracia Castro-Luna ◽  
Diana Jiménez-Rodríguez ◽  
Ana Belén Castaño-Fernández ◽  
Antonio Pérez-Rueda
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Corneal Thickness ◽  
Cross Sectional Study ◽  
Machine Learning Techniques ◽  
Critical Determinant ◽  
Cross Sectional ◽  
Essential Variable ◽  
Learning Techniques ◽  
Initial Stage

(1) Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a machine learning technique for classification based on the construction of thousands of decision trees. The aim of this study was to use the random forest technique in the classification and prediction of subclinical keratoconus, considering the metrics proposed by Pentacam and Corvis. (2) Methods: The design was a retrospective cross-sectional study. A total of 81 eyes of 81 patients were enrolled: sixty-one eyes with healthy corneas and twenty patients with subclinical keratoconus (SCKC): This initial stage includes patients with the following conditions: (1) minor topographic signs of keratoconus and suspicious topographic findings (mild asymmetric bow tie, with or without deviation; (2) average K (mean corneal curvature) <46, 5 D; (3) minimum corneal thickness (ECM) > 490 μm; (4) no slit lamp found; and (5) contralateral clinical keratoconus of the eye. Pentacam topographic and Corvis biomechanical variables were collected. Decision tree and random forest were used as machine learning techniques for classifications. Random forest performed a ranking of the most critical variables in classification. (3) Results: The essential variable was SP A1 (stiffness parameter A1), followed by A2 time, posterior coma 0º, A2 velocity and peak distance. The model efficiently predicted all patients with subclinical keratoconus (Sp = 93%) and was also a good model for classifying healthy cases (Sen = 86%). The overall accuracy rate of the model was 89%. (4) Conclusions: The random forest model was a good model for classifying subclinical keratoconus. The SP A1 variable was the most critical determinant in classifying and identifying subclinical keratoconus, followed by A2 time.

scholarly journals Identification of Paddy Leaf Diseases using Evolutionary and Machine Learning Methods

2021 ◽  
Vol 12 (2) ◽  
pp. 1672-1686
Author(s):  
Nilam Sachin Patil
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Plant Diseases ◽  
Automated System ◽  
Machine Learning Techniques ◽  
Nearest Neighbour ◽  
Agricultural Plant ◽  
Machine Learning Methods ◽  
Cascaded Classifiers ◽  
Learning Techniques

In the field of agriculture, especially paddy plants, there is a demand for research to classify the paddy diseases at early stages. This is feasible if there are automated systems that can assist the farmers to recognize the paddy diseases from the paddy leaf images of the plants. The recognition of agricultural plant diseases by utilizing the image-processing and machine learning techniques can certainly minimize the reliance on the farmers to protect the yield of paddy crops. In this paper, an attempt has been made to pre-process the images to prepare the feature-set for Classifiers and then feature extraction algorithms are used to extract the relevant features from the processed images. The feature-set is then supplied to the classifiers for identification of Paddy Leaf diseases. The usage of cascaded classifiers has been explored to detect the diseases of paddy leaves. An attempt has also been made to use genetic algorithm with nearest neighbour algorithm to identify the diseases of paddy leaves. The proposed automated system can be used on Android , Windows platform and Apple platform for quickly identifying the paddy leaf diseases as the entire implementation has been performed using MATLAB. The proposed automated system can certainly help the farmers to classify the diseased paddy leaves at early stage to protect the crops from further damage.

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