Simple Machine Learning Methods Research Articles

Искусственный интеллект в диагностике и лечении мочекаменной болезни.

Complex health-care data will be growing exponentially as we enter the era of “big data”. Interpretation and analysis of a large amount of medical data for rapid and personalized decision-making require artificial intelligence-driven technologies and data mining techniques. Over the past decade, a rapid transition to the analysis, treatment and monitoring of kidney stone disease using the artificial intelligence (AI) with machine learning algorithms and artificial neural networks ensures the development of precise support systems. Machine learning algorithms able to predict the location of stones using computed tomography scans or ultrasound images, determine their chemical composition, recurrence rate based on demographic and genetic variables, spontaneous ureteral calculous passage, and treatment outcomes. Recent studies use rather simple machine learning methods and small training sets that reduce their accuracy, sensitivity, and specificity and limit their routine application. Therefore, the development of complex architectures, using multilayer artificial neural networks and convolutional neural networks, and integration other heterogeneous variables will improve the resultant predictive accuracy ensuring their introduction in real clinical practice.

Hilbert series, machine learning, and applications to physics

We describe how simple machine learning methods successfully predict geometric properties from Hilbert series (HS). Regressors predict embedding weights in projective space to ∼1 mean absolute error, whilst classifiers predict dimension and Gorenstein index to >90% accuracy with ∼0.5% standard error. Binary random forest classifiers managed to distinguish whether the underlying HS describes a complete intersection with high accuracies exceeding 95%. Neural networks (NNs) exhibited success identifying HS from a Gorenstein ring to the same order of accuracy, whilst generation of “fake” HS proved trivial for NNs to distinguish from those associated to the three-dimensional Fano varieties considered.

Random forest model identifies serve strength as a key predictor of tennis match outcome

Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of particular interest to bettors and betting companies alike is potential use of sports records to predict tennis match outcomes prior to match start. We compiled, cleaned, and used the largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods. Using such methods allows for rapid fit and prediction times to readily incorporate new data and make real-time predictions. We were able to predict match outcomes with upwards of 80%accuracy, much greater than predictions using betting odds alone, and identify serve strength as a key predictor of match outcome. By combining prediction accuracies from three models, we were able to nearly recreate a probability distribution based on average betting odds from betting companies, which indicates that betting companies are using similar information to assign odds to matches. These results demonstrate the capability of relatively simple machine learning models to quite accurately predict tennis match outcomes.

Application of Wavelet Scattering and Machine Learning on Structural Health Diagnosis for Quadcopter

The aim of this study was to examine the health diagnosis classification method of quadcopter structures with different mixed faults. The loosening of the motor mount, damage to the propeller, and the loosening of the arm mount were the main fault conditions investigated. Data were first acquired under non-fault conditions and the conditions of the three types of fault. Then, the features of the vibration and pulse width modulation signals were extracted by root mean square, standard deviation, and sample entropy. Moreover, the features of the audio signal were extracted by wavelet scattering, which contains time-frequency domain information that provides significant power for classification. In this paper, we propose a simple machine learning method, based on the k-Nearest Neighbor (kNN), not only for classification but also demonstrating the efficacy of the features. To test the limits of accuracy, different configurations of kNN parameters are deployed, in addition to the features. In summary, as a result of the highly efficacious features, despite mixed fault conditions, the accuracy reached 90.73%. This method improves the accuracy of mixed faults’ classification and maintains a certain level of classification effectiveness.

Pantograph Sliding Strips Failure-Reliability Assessment and Damage Reduction Method Based on Decision Tree Model.

Damage to the pantograph or sliding strip may cause the blocking of the railway line. This is the main reason for which the prediction of pantographs’ failure is important for railway carriers and researchers. This article presents a sliding strips failure prediction method as a main means of preventing disruptions to the transport chain. To develop the best predictive model based on the decision tree, the complex tree, medium tree and simple tree machine learning methods were tested. Using a decision tree, the categorization of the given technical conditions can be properly realized. The obtained results showed that the presented model can reduce sliding strip failure by up to 50%. Special attention was paid to the current collector (AKP-4E, 5ZL type), measured during periodic reviews of locomotives EU07 and EU09. To assess the reliability of the selected pantograph strips, a non-destructive degradation analysis was carried out. On the basis of the wear measurements of the strips and the critical value of wear, a failure distribution model was developed. Operational data, collected during periodic technical reviews, were provided by one of the biggest railway carriers in Poland. The results of the performed analyses may be used to build a preventive maintenance strategy to protect pantographs. The applied reliability models of wear propagation can be extended by the parameters of the cost and repair time becoming the basis for estimating the costs of operation and maintenance.

P57.04 Predicting Treatment Response to 1st- line Pembrolizumab in Advanced Non-Small Cell Lung Cancer (NSCLC) Patients with High PDL1 Expression

Combination of pembrolizumab and chemotherapy has shown improved survival in advanced NSCLC compared to chemotherapy alone. However, the overall response rate has only increased from 45% to 60% by adding chemotherapy to pembrolizumab alone for patients with high PDL1 expression (PD-L1 ≥50%). Currently no biomarker or clinicopathological features can be used to identify the subgroup with tumors PD-L1 ≥ 50% who would respond well to pembrolizumab alone in the 1st-line setting. We hypothesize a machine learning model trained on pre-treatment CT image radiomic features can be used as a digital biomarker to select the aforementioned subgroup, thereby reducing chemotherapy related toxicity and mitigating health care expenditure. We retrospectively identified stage III/IV NSCLC patients who received 1st-line single pembrolizumab at British Columbia Cancer (Vancouver, Canada), with PDL1 ≥50% and all had baseline staging CT within 6 weeks prior to starting immunotherapy and the 1st follow-up (FU) CT within 12 weeks after treatment initiation. The baseline and 1st follow-up CTs were reviewed by two oncological radiologists to identified the lung tumors on the baseline CT images and assess tumor response (i.e., partial response, PR vs progressive disease, PD) using RECIST 1.1 criteria. Utilizing an in-house CT Otsu based thresholding segmentation program and radiomic feature extraction pipeline, we identified potentially discriminating features from CT lung tumor images. We performed sequential forward feature selection to identify the 10 most highly classifying features from discrete shape, texture, and intensity radiomic features generated for 3 distinct tumor segmentation masks (lesion core, core with perimeter transition pixels, and the ring of parenchymal tissue surrounding the lesion). In this study, we leveraged an 8-Xfold validation Linear Discriminate Analysis (LDA), a simple machine learning method trained on these 10 features, to discriminate tumor response to 1st-line pembrolizumab as evaluated at FU CT scans. Sixty-eight patients were included (44% male, 73±6 yrs, 52ES/12CS/4NS; pack years: 43±18; ECOG range: 0-3). Twenty-eight showed response to the 1st-line pembrolizumab and forty had progressive disease. We identified eighty-four lung tumors in these patients which we extracted radiomic features from the five central slices, providing us with a pseudo-volumetric CT image training and test set (N=380). ROC analysis of the LDA model resulted in an accuracy of 79.4% [SN: 0.882, SP: 0.684] (AUC: 0.79) for our patient dataset. Given our class label imbalance, we performed precision-recall (PPV vs Sensitivity) analysis to identify sources of class bias (AUC vs No-skill AUC: 0.80 vs 0.59). Our data showed that a rudimentary and interpretable machine learning method can lead to appropriate treatment paths for advanced stage lung cancers. A combination of all mask features performed well on this task with a majority of the features selected being texture and intensity features from the core plus edge transitionary boundary pixels. Future analysis will be conducted using other machine learning methods to validate these findings, while keeping the LDA as the primary analysis method.

RDDSVM: accurate prediction of A-to-I RNA editing sites from sequence using support vector machines.

Adenosine to inosine (A-to-I) editing in RNA is involved in various biological processes like gene expression, alternative splicing, and mRNA degradation associated with carcinogenesis and various human diseases. Therefore, accurate identification of RNA editing sites in transcriptome is valuable for research and medicine. RNA-seq is very useful for the detection of RNA editing events in condition-specific cells. However, computational analysis methods of RNA-seq data have considerable false-positive risks due to mapping errors. In this study, we developed a simple machine learning method using support vector machines to train sequence and structure information derived from flanking sequences of experimentally verified A-to-I editing sites to predict new A-to-I editing sites in RNA. The highest performance results were obtained by the model that utilizes the composition of the triplet sequence elements in the flanking regions of the in A-to-I editing sites. Using this model, the SVM classifier also showed high performance on experimentally verified data providing a sensitivity of 92.8%, specificity of 77.1%, and accuracy of 90.2%. To compare the predictive capacity of our method with other classifiers that use sequence information, we have used validated human A-to-I RNA editing sites by Sanger sequencing. Out of 58 validated editing sites, our method recognized 53 of them correctly with an accuracy of 91.4% outperforming other classifiers. As to our knowledge, this is the first case of utilization of the composition of the triplet sequence elements neighboring A-to-I editing sites for the prediction of new A-to-I editing sites in RNA. The methodology is very easy to perform and computationally low demanding making it a convenient and valuable choice for facilities with low sources. To facilitate the usage of the method publicly, we developed an open-source program called RDDSVM to perform prediction on candidate A-to-I RNA editing sites using support vector machines.

ASTRO

We present the design, implementation, and experimental evaluation of ASTRO, a modular end-to-end system for distributed sensing missions with autonomous networked drones. We introduce the fundamental system architecture features that enable agnostic sensing missions on top of the ASTRO drones. We demonstrate the key principles of ASTRO by using on-board software-defined radios to find and track a mobile radio target. We show how simple distributed on-board machine learning methods can be used to find and track a mobile target, even if all drones lose contact with a ground control. Also, we show that ASTRO is able to find the target even if it is hiding under a three-ton concrete slab, representing a highly irregular propagation environment. Our findings reveal that, despite no prior training and noisy sensory measurements, ASTRO drones are able to learn the propagation environment in the scale of seconds and localize a target with a mean accuracy of 8 m. Moreover, ASTRO drones are able to track the target with relatively constant error over time, even as it moves at a speed close to the maximum drone speed.

A hybrid data-driven simultaneous fault diagnosis model for air handling units

Simultaneous faults are situations where two or more faults occur at the same time, which are difficult to be diagnosed by simple and stand-alone standard machine learning methods as a multi-label problem. Simultaneous faults for HVAC systems are not given enough attention under the challenges of insufficient sensors, coupled faults, and sophisticated mathematical models. A novel simultaneous fault diagnosis model based on a hybrid method of classifier chains integrated with random forest (CC-RF) is proposed in this study. On-site experiments involving six single fault cases and seven simultaneous fault cases for an air handling unit (AHU) system are conducted to verify this model. The results demonstrate a satisfactory performance with the test accuracy of 99.50% and F1 score of 99.66% for the fault diagnosis model. The model is proven to be neither underfitting nor overfitting and can be scalable with a reasonable training time. Through online analysis, the proposed method demonstrates a good competence of diagnosing not only single faults but also simultaneous fault. The CC-RF method has a better performance compared with classifier chains with logistic regression and support vector machine. Besides, the proposed method of classifier chains outperforms binary relevance due to the benefitting of label relevance.

An AI-enabled lightweight data fusion and load optimization approach for Internet of Things

In the densely populated Internet of Things (IoT) applications, sensing range of the nodes might overlap frequently. In these applications, the nodes gather highly correlated and redundant data in their vicinity. Processing these data depletes the energy of nodes and their upstream transmission towards remote datacentres, in the fog infrastructure, may result in an unbalanced load at the network gateways and edge servers. Due to heterogeneity of edge servers, few of them might be overwhelmed while others may remain less-utilized. As a result, time-critical and delay-sensitive applications may experience excessive delays, packet loss, and degradation in their Quality of Service (QoS). To ensure QoS of IoT applications, in this paper, we eliminate correlation in the gathered data via a lightweight data fusion approach. The buffer of each node is partitioned into strata that broadcast only non-correlated data to edge servers via the network gateways. Furthermore, we propose a dynamic service migration technique to reconfigure the load across various edge servers. We assume this as an optimization problem and use two meta-heuristic algorithms, along with a migration approach, to maintain an optimal Gateway-Edge configuration in the network. These algorithms monitor the load at each server, and once it surpasses a threshold value (which is dynamically computed with a simple machine learning method), an exhaustive search is performed for an optimal and balanced periodic reconfiguration. The experimental results of our approach justify its efficiency for large-scale and densely populated IoT applications.

Renewable energy power generation forecasting using deep learning method

Smart Micro Grid in household areas aims to meet electricity needs through the integration between state power plant with renewable energy sources so that the electricity used does not depend entirely on state utility. Smart Micro Grid also enables the availability of energy management services supported by Machine Learning (ML) technology, Big Data, Artificial Intelligence (AI), Internet of Things (IoT) and smart sensors so that consumer use of electricity is more efficient. To improve energy management services and distribution of renewable energy sources, new innovations in ML technology are needed to produce accurate learning models that can be used in the energy analysis process, such as monitoring, prediction, forecasting, scheduling and decision-making. However, the complexity of the problems in the smart grid system, which includes uncertainty and non-linearity, affects the more complex the energy data structure generated. Therefore, the simple ML method will not be able to perform the Learning process because it is limited to simple raw data processing. Therefore, the Deep Learning (DL) method can be used as a Learning method on data that has a complex and large structure. In this paper, Deep Neural Network (DNN) method will be developed using Long Short-Term Memory (LSTM) as a Learning model to provide Future Accurate Prediction (FAP) on electricity use and on renewable energy plants. Prediction test using Confusion Matrix accuracy value and RMSE error value

Cerebral Infarction Classification Using the K-Nearest Neighbor and Naive Bayes Classifier

Cerebral infarction is one of the causes of stroke in the brain and is included in ischemic stroke. To detect infarction in the brain, classification in machine learning can be used. They are k-Nearest Neighbor (kNN) and Naive Bayes (NB). kNN is a simple and well-known machine learning method with high accuracy values. however, kNN can produce sub-optimal results if very little training data is used. Because it will produce accuracy from a biased model and has less than optimal performance. Meanwhile, Naive Bayes Classifier has a better level of accuracy compared to other classifier models. And only requires a small training test to get high accuracy. Therefore, this study will compare 2 different classifications to get the highest accuracy in the brain infarction dataset obtained from the Department of Radiology, dr. Cipto Mangunkusumo Hospital (RSCM). The accuracy of this method reaches 91% for kNN and 97% for Naive Bayes.

Tomato Growth State Map for the Automation of Monitoring and Harvesting

To realize smart agriculture, we engaged in its systematization, from monitoring to harvesting tomato fruits using robots. In this paper, we explain a method of generating a map of the tomato growth states to monitor the various stages of tomato fruits and decide a harvesting strategy for the robots. The tomato growth state map visualizes the relationship between the maturity stage, harvest time, and yield. We propose a generation method of the tomato growth state map, a recognition method of tomato fruits, and an estimation method of the growth states (maturity stages and harvest times). For tomato fruit recognition, we demonstrate that a simple machine learning method using a limited learning dataset and the optical properties of tomato fruits on infrared images exceeds more complex convolutional neural network, although the results depend on how the training dataset is created. For the estimation of the growth states, we conducted a survey of experienced farmers to quantify the maturity stages into six classifications and harvest times into three terms. The growth states were estimated based on the survey results. To verify the tomato growth state map, we conducted experiments in an actual tomato greenhouse and herein report the results.

Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada

Abstract. Snow is a critical contributor to Ontario's water-energy budget, with impacts on water resource management and flood forecasting. Snow water equivalent (SWE) describes the amount of water stored in a snowpack and is important in deriving estimates of snowmelt. However, only a limited number of sparsely distributed snow survey sites (n=383) exist throughout Ontario. The SNOw Data Assimilation System (SNODAS) is a daily, 1 km gridded SWE product that provides uniform spatial coverage across this region; however, we show here that SWE estimates from SNODAS display a strong positive mean bias of 50 % (16 mm SWE) when compared to in situ observations from 2011 to 2018. This study evaluates multiple statistical techniques of varying complexity, including simple subtraction, linear regression and machine learning methods to bias-correct SNODAS SWE estimates using absolute mean bias and RMSE as evaluation criteria. Results show that the random forest (RF) algorithm is most effective at reducing bias in SNODAS SWE, with an absolute mean bias of 0.2 mm and RMSE of 3.64 mm when compared with in situ observations. Other methods, such as mean bias subtraction and linear regression, are somewhat effective at bias reduction; however, only the RF method captures the nonlinearity in the bias and its interannual variability. Applying the RF model to the full spatio-temporal domain shows that the SWE bias is largest before 2015, during the spring melt period, north of 44.5∘ N and east (downwind) of the Great Lakes. As an independent validation, we also compare estimated snowmelt volumes with observed hydrographs and demonstrate that uncorrected SNODAS SWE is associated with unrealistically large volumes at the time of the spring freshet, while bias-corrected SWE values are highly consistent with observed discharge volumes.

Toward Predicting Infant Developmental Outcomes From Day-Long Inertial Motion Recordings

As improvements in medicine lower infant mortality rates, more infants with neuromotor challenges survive past birth. The motor, social, and cognitive development of these infants are closely interrelated, and challenges in any of these areas can lead to developmental differences. Thus, analyzing one of these domains - the motion of young infants - can yield insights on developmental progress to help identify individuals who would benefit most from early interventions. In the presented data collection, we gathered day-long inertial motion recordings from N = 12 typically developing (TD) infants and N = 24 infants who were classified as at risk for developmental delays (AR) due to complications at or before birth. As a first research step, we used simple machine learning methods (decision trees, k-nearest neighbors, and support vector machines) to classify infants as TD or AR based on their movement recordings and demographic data. Our next aim was to predict future outcomes for the AR infants using the same simple classifiers trained from the same movement recordings and demographic data. We achieved a 94.4% overall accuracy in classifying infants as TD or AR, and an 89.5% overall accuracy predicting future outcomes for the AR infants. The addition of inertial data was much more important to producing accurate future predictions than identification of current status. This work is an important step toward helping stakeholders to monitor the developmental progress of AR infants and identify infants who may be at the greatest risk for ongoing developmental challenges.

An Improved KNN-Based Slope Stability Prediction Model

An accurate slope prediction model is important for slope reinforcement before the disaster. The k-nearest neighbor (KNN) algorithm, as a simple and effective nonparametric machine learning method, is widely applied in classification recognition. In our study, the k-nearest neighbor (KNN) algorithm is improved to reduce its sample dependence and improve the robustness of the algorithm, and then the prediction model of the slope stability is proposed based on the improved k-nearest neighbor (KNN) algorithm. Extensive experimental results show that our proposed prediction model achieves high prediction performance in this regard. Moreover, a comparison between our proposed prediction model and the finite element method, which is the classical theoretical method of slope stability, was made, which will provide an important approach to predicting the slope stability for slope engineering. Finally, shaking table test of a slope model is conducted to evaluate whether the slope is stable or not, and the experimental results are in good agreement with the prediction results of our proposed prediction model, which further demonstrates its effectiveness.

Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports.

Accurate, automated extraction of clinical stroke information from unstructured text has several important applications. ICD-9/10 codes can misclassify ischemic stroke events and do not distinguish acuity or location. Expeditious, accurate data extraction could provide considerable improvement in identifying stroke in large datasets, triaging critical clinical reports, and quality improvement efforts. In this study, we developed and report a comprehensive framework studying the performance of simple and complex stroke-specific Natural Language Processing (NLP) and Machine Learning (ML) methods to determine presence, location, and acuity of ischemic stroke from radiographic text. We collected 60,564 Computed Tomography and Magnetic Resonance Imaging Radiology reports from 17,864 patients from two large academic medical centers. We used standard techniques to featurize unstructured text and developed neurovascular specific word GloVe embeddings. We trained various binary classification algorithms to identify stroke presence, location, and acuity using 75% of 1,359 expert-labeled reports. We validated our methods internally on the remaining 25% of reports and externally on 500 radiology reports from an entirely separate academic institution. In our internal population, GloVe word embeddings paired with deep learning (Recurrent Neural Networks) had the best discrimination of all methods for our three tasks (AUCs of 0.96, 0.98, 0.93 respectively). Simpler NLP approaches (Bag of Words) performed best with interpretable algorithms (Logistic Regression) for identifying ischemic stroke (AUC of 0.95), MCA location (AUC 0.96), and acuity (AUC of 0.90). Similarly, GloVe and Recurrent Neural Networks (AUC 0.92, 0.89, 0.93) generalized better in our external test set than BOW and Logistic Regression for stroke presence, location and acuity, respectively (AUC 0.89, 0.86, 0.80). Our study demonstrates a comprehensive assessment of NLP techniques for unstructured radiographic text. Our findings are suggestive that NLP/ML methods can be used to discriminate stroke features from large data cohorts for both clinical and research-related investigations.

Ensemble Learning Models Based on Noninvasive Features for Type 2 Diabetes Screening: Model Development and Validation.

BackgroundEarly diabetes screening can effectively reduce the burden of disease. However, natural population–based screening projects require a large number of resources. With the emergence and development of machine learning, researchers have started to pursue more flexible and efficient methods to screen or predict type 2 diabetes.ObjectiveThe aim of this study was to build prediction models based on the ensemble learning method for diabetes screening to further improve the health status of the population in a noninvasive and inexpensive manner.MethodsThe dataset for building and evaluating the diabetes prediction model was extracted from the National Health and Nutrition Examination Survey from 2011-2016. After data cleaning and feature selection, the dataset was split into a training set (80%, 2011-2014), test set (20%, 2011-2014) and validation set (2015-2016). Three simple machine learning methods (linear discriminant analysis, support vector machine, and random forest) and easy ensemble methods were used to build diabetes prediction models. The performance of the models was evaluated through 5-fold cross-validation and external validation. The Delong test (2-sided) was used to test the performance differences between the models.ResultsWe selected 8057 observations and 12 attributes from the database. In the 5-fold cross-validation, the three simple methods yielded highly predictive performance models with areas under the curve (AUCs) over 0.800, wherein the ensemble methods significantly outperformed the simple methods. When we evaluated the models in the test set and validation set, the same trends were observed. The ensemble model of linear discriminant analysis yielded the best performance, with an AUC of 0.849, an accuracy of 0.730, a sensitivity of 0.819, and a specificity of 0.709 in the validation set.ConclusionsThis study indicates that efficient screening using machine learning methods with noninvasive tests can be applied to a large population and achieve the objective of secondary prevention.

Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants.

The normalized difference vegetation index (NDVI) is widely used in remote sensing to monitor plant growth and chlorophyll levels. Usually, a multispectral camera (MSC) or hyperspectral camera (HSC) is required to obtain the near-infrared (NIR) and red bands for calculating NDVI. However, these cameras are expensive, heavy, difficult to geo-reference, and require professional training in imaging and data processing. On the other hand, the RGBN camera (NIR sensitive RGB camera, simply modified from standard RGB cameras by removing the NIR rejection filter) have also been explored to measure NDVI, but the results did not exactly match the NDVI from the MSC or HSC solutions. This study demonstrates an improved NDVI estimation method with an RGBN camera-based imaging system (Ncam) and machine learning algorithms. The Ncam consisted of an RGBN camera, a filter, and a microcontroller with a total cost of only $70 ~ 85. This new NDVI estimation solution was compared with a high-end hyperspectral camera in an experiment with corn plants under different nitrogen and water treatments. The results showed that the Ncam with two-band-pass filter achieved high performance (R2 = 0.96, RMSE = 0.0079) at estimating NDVI with the machine learning model. Additional tests showed that besides NDVI, this low-cost Ncam was also capable of predicting corn plant nitrogen contents precisely. Thus, Ncam is a potential option for MSC and HSC in plant phenotyping projects.

Simple Screening Method of Maize Disease using Machine Learning

Plant leaf diseases are significant issue in agriculture field. Some of the common plant leaf diseases are powdery mildew, dark spot and rust. They are a noteworthy wellspring of an immense number of dollar worth of setbacks to farmers on a yearly premise. Plant breeders frequently need to screen countless number of plant leaves to find the stage of diseases of their crops to perform an early treatments. Therefore, a robust method for field screening is needed in order to spare the farmers and the environment as well. Inappropriate used of treatments such as impulsive pesticides can imperil the environment. Hence, this paper present a simple and efficient machine learning method which is Fuzzy C-Means algorithm to screen leaf disease severity in maize. Fuzzy C-Means is a new algorithm and very efficient to be used in object detection. Therefore, it is applicable to detect disease spot in plant leaf and measure the diseases severity. This field screening method help the farmer to identify the progression of the diseases in their crops quicker and easier than the other field screening techniques.