scholarly journals Smarter Robotic Sprayer System for Precision Agriculture

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2061
Author(s):  
André Rodrigues Baltazar ◽  
Filipe Neves dos Santos ◽  
António Paulo Moreira ◽  
António Valente ◽  
José Boaventura Cunha
Keyword(s):  
Precision Agriculture ◽  
Vegetation Index ◽  
Reference Value ◽  
Support Vector ◽  
Svm Classifier ◽  
Accuracy Score ◽  
Accuracy And Precision ◽  
Perception System ◽  
Water Rate ◽  
Leaf Density

The automation of agricultural processes is expected to positively impact the environment by reducing waste and increasing food security, maximising resource use. Precision spraying is a method used to reduce the losses during pesticides application, reducing chemical residues in the soil. In this work, we developed a smart and novel electric sprayer that can be assembled on a robot. The sprayer has a crop perception system that calculates the leaf density based on a support vector machine (SVM) classifier using image histograms (local binary pattern (LBP), vegetation index, average, and hue). This density can then be used as a reference value to feed a controller that determines the air flow, the water rate, and the water density of the sprayer. This perception system was developed and tested with a created dataset available to the scientific community and represents a significant contribution. The results of the leaf density classifier show an accuracy score that varies between 80% and 85%. The conducted tests prove that the solution has the potential to increase the spraying accuracy and precision.

scholarly journals Machine Learning-Based Crop Drought Mapping System by UAV Remote Sensing RGB Imagery

2020 ◽  
Vol 08 (01) ◽  
pp. 71-83 ◽  
Author(s):  
Jinya Su ◽  
Matthew Coombes ◽  
Cunjia Liu ◽  
Yongchao Zhu ◽  
Xingyang Song ◽  
...  
Keyword(s):  
Water Stress ◽  
Vegetation Index ◽  
Field Measurements ◽  
Vital Role ◽  
Learning System ◽  
Bayesian Optimization ◽  
Growth And Yield ◽  
Support Vector ◽  
Svm Classifier ◽  
Spectral Intensities

Water stress has adverse effects on crop growth and yield, where its monitoring plays a vital role in precision crop management. This paper aims at initially exploiting the potentials of UAV aerial RGB image in crop water stress assessment by developing a simple but effective supervised learning system. Various techniques are seamlessly integrated into the system including vegetation segmentation, feature engineering, Bayesian optimization and Support Vector Machine (SVM) classifier. In particular, wheat pixels are first segmented from soil background by using the classical vegetation index thresholding. Rather than performing pixel-wise classification, pixel squares of appropriate dimension are defined as samples, from which various features for pure vegetation pixels are extracted including spectral and color index (CI) features. SVM with Bayesian optimization is adopted as the classifier. To validate the developed system, a Unmanned Aerial Vehicle (UAV) survey is performed to collect high-resolution atop canopy RGB imageries by using DJI S1000 for the experimental wheat fields of Gucheng town, Heibei Province, China. Two levels of soil moisture were designed after seedling establishment for wheat plots by using intelligent irrigation and rain shelter, where field measurements were to obtain ground soil water ratio for each wheat plot. Comparative experiments by three-fold cross-validation demonstrate that pixel-wise classification, with a high computation load, can only achieve an accuracy of 82.8% with poor F1 score of 71.7%; however, the developed system can achieve an accuracy of 89.9% with F1 score of 87.7% by using only spectral intensities, and the accuracy can be further improved to 92.8% with F1 score of 91.5% by fusing both spectral intensities and CI features. Future work is focused on incorporating more spectral information and advanced feature extraction algorithms to further improve the performance.

scholarly journals Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model

2021 ◽  
Vol 3 (1) ◽  
pp. 9-14
Author(s):  
Tsehay Admassu Assegie
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Liver Disease ◽  
Classification Model ◽  
Support Vector ◽  
Disease Prediction ◽  
Accuracy Score ◽  
Learning Models ◽  
Accuracy And Precision ◽  
Machine Learning Models

Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning algorithms and we have evaluated the accuracy and precision of the models on liver disease prediction using the Indian liver disease data repository. The analysis of result showed 82.90% accuracy for SVM and 72.64% accuracy for the KNN algorithm. Based on the accuracy score of SVM and KNN on experimental test results, the SVM is better in performance on the liver disease prediction than the KNN algorithm.  

scholarly journals Mapping Maize Cropping Patterns in Dak Lak, Vietnam Through MODIS EVI Time Series

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 478
Author(s):  
Ha Thi Thu Nguyen ◽  
Loc Van Nguyen ◽  
C.A.J.M (Kees) de Bie ◽  
Ignacio A. Ciampitti ◽  
Duc Anh Nguyen ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Vegetation Index ◽  
Support Vector ◽  
Svm Classifier ◽  
Cropping Pattern ◽  
Maize Crop ◽  
Cropping Patterns ◽  
Modis Evi ◽  
Land Use Maps

Land use maps specifying up-to-date acreage information on maize (Zea mays L.) cropping patterns are required by many stakeholders in Vietnam. Government statistics, however, lag behind by one year, and the official land use maps are only updated at 5-year intervals. The aim of this study was to apply the Savitzky–Golay algorithm to reconstruct noisy Enhanced Vegetation Index (EVI) time series (2003–2018) from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) to allow timely detection of changes in maize crop phenology, and then to employ a linear kernel Support Vector Machine (SVM) classifier on the reconstructed EVI time series to prepare the present-day maize cropping pattern map of Dak Lak province of Vietnam. The method was able to specify the spatial extent of areas cropped to maize with an overall map accuracy of 79% and could also differentiate the areas cropped to maize just once versus twice annually. The by-district mapped maize acreage shows a good agreement with the official governmental data, with a 0.93 correlation coefficient (r) and a root mean square deviation (RMSD) of 1624 ha.

scholarly journals Comparing methods to estimate perennial ryegrass biomass: canopy height and spectral vegetation indices

2020 ◽  
Vol 22 (1) ◽  
pp. 205-225 ◽  
Author(s):  
Gustavo Togeiro de Alckmin ◽  
Lammert Kooistra ◽  
Richard Rawnsley ◽  
Arko Lucieer
Keyword(s):  
Perennial Ryegrass ◽  
Precision Agriculture ◽  
Vegetation Indices ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Biomass Estimation ◽  
Pasture Management ◽  
Accuracy And Precision ◽  
Spectral Vegetation Indices ◽  
Regression Techniques

AbstractPasture management is highly dependent on accurate biomass estimation. Usually, such activity is neglected as current methods are time-consuming and frequently perceived as inaccurate. Conversely, spectral data is a promising technique to automate and improve the accuracy and precision of estimates. Historically, spectral vegetation indices have been widely adopted and large numbers have been proposed. The selection of the optimal index or satisfactory subset of indices to accurately estimate biomass is not trivial and can influence the design of new sensors. This study aimed to compare a canopy-based technique (rising plate meter) with spectral vegetation indices. It examined 97 vegetation indices and 11,026 combinations of normalized ratio indices paired with different regression techniques on 900 pasture biomass data points of perennial ryegrass (Lolium perenne) collected throughout a 1-year period. The analyses demonstrated that the canopy-based technique is superior to the standard normalized difference vegetation index (∆, 115.1 kg DM ha−1 RMSE), equivalent to the best performing normalized ratio index and less accurate than four selected vegetation indices deployed with different regression techniques (maximum ∆, 231.1 kg DM ha−1). When employing the four selected vegetation indices, random forests was the best performing regression technique, followed by support vector machines, multivariate adaptive regression splines and linear regression. Estimate precision was improved through model stacking. In summary, this study demonstrated a series of achievable improvements in both accuracy and precision of pasture biomass estimation, while comparing different numbers of inputs and regression techniques and providing a benchmark against standard techniques of precision agriculture and pasture management.

scholarly journals Influence of Pre-Processing Strategies on the Performance of ML Classifiers Exploiting TF-IDF and BOW Features

2020 ◽  
Vol 9 (2) ◽  
pp. 49-68
Author(s):  
Amit Purushottam Pimpalkar ◽  
R. Jeberson Retna Raj
Keyword(s):  
Positive Impact ◽  
Support Vector ◽  
Svm Classifier ◽  
Features Selection ◽  
Data Set ◽  
Processing Strategies ◽  
Document Frequency ◽  
Accuracy And Precision ◽  
The Right ◽  
Processing Techniques

Data analytics and its associated applications have recently become impor-tant fields of study. The subject of concern for researchers now-a-days is a massive amount of data produced every minute and second as people con-stantly sharing thoughts, opinions about things that are associated with them. Social media info, however, is still unstructured, disseminated and hard to handle and need to be developed a strong foundation so that they can be utilized as valuable information on a particular topic. Processing such unstructured data in this area in terms of noise, co-relevance, emoticons, folksonomies and slangs is really quite challenging and therefore requires proper data pre-processing before getting the right sentiments. The dataset is extracted from Kaggle and Twitter, pre-processing performed using NLTK and Scikit-learn and features selection and extraction is done for Bag of Words (BOW), Term Frequency (TF) and Inverse Document Frequency (IDF) scheme. For polarity identification, we evaluated five different Machine Learning (ML) algorithms viz Multinomial Naive Bayes (MNB), Logistic Regression (LR), Decision Trees (DT), XGBoost (XGB) and Support Vector Machines (SVM). We have performed a comparative analysis of the success for these algorithms in order to decide which algorithm works best for the given data-set in terms of recall, accuracy, F1-score and precision. We assess the effects of various pre-processing techniques on two datasets; one with domain and other not. It is demonstrated that SVM classifier outperformed the other classifiers with superior evaluations of 73.12% and 94.91% for accuracy and precision respectively. It is also highlighted in this research that the selection and representation of features along with various pre-processing techniques have a positive impact on the performance of the classification.  The ultimate outcome indicates an improvement in sentiment classification and we noted that pre-processing approaches obviously suggest an improvement in the efficiency of the classifiers.

scholarly journals A Tale of Grass and Trees: Characterizing Vegetation Change in Payne’s Creek National Park, Belize from 1975 to 2019

2020 ◽  
Vol 10 (12) ◽  
pp. 4356 ◽  
Author(s):  
Luke Blentlinger ◽  
Hannah V. Herrero
Keyword(s):  
Time Series ◽  
National Park ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Imagery ◽  
Support Vector ◽  
Svm Classifier ◽  
Remotely Sensed Data ◽  
Svm Classification

The lowland savannas of Belize are important areas to conserve for their biodiversity. This study takes place in Payne’s Creek National Park (PCNP) in the southern coastal plain of Belize. PCNP protects diverse terrestrial and coastal ecosystems, unique physical features, and wildlife. A Support Vector Machine (SVM) classification technique was used to classify the heterogeneous landscape of PCNP to characterize woody and non-woody conversion in a time-series of remotely sensed data from 1975, 1993, 2011 and 2019. Results indicate that the SVM classifier performs well in this small savanna landscape (average overall accuracy of 91.9%) with input variables of raw Landsat imagery, the Normalized Difference Vegetation Index (NDVI), elevation, and soil type. Our change trajectory analysis shows that PCNP is a relatively stable landscape, but with certain areas that are prone to multiple conversions in the time-series. Woody vegetation mostly occurs in areas with variable slopes and riparian zones with increased nutrient availability. This study does not show extensive woody conversion in PCNP, contrary to widespread woody encroachment that is occurring in savannas on other continents. These high-performing SVM classification maps and future studies will be an important resource of information on Central American savanna vegetation dynamics for savanna scientists and land managers that use adaptive management for ecosystem preservation.

scholarly journals Improving the Efficiency of Heart Disease Prediction Using SVM and a Novel Tree Specific Random Forest Classifier (NTSRF)

2021 ◽  
Vol 36 (1) ◽  
pp. 616-622
Author(s):  
P. Harish ◽  
Dr.R. Sabitha
Keyword(s):  
Heart Disease ◽  
Random Forest ◽  
Random Forest Classifier ◽  
Support Vector ◽  
Svm Classifier ◽  
Disease Prediction ◽  
Data Set ◽  
Medical Sector ◽  
Accuracy And Precision ◽  
Better Than

Aim: The objective of the work is to evaluate the accuracy and precision in predicting the heart disease using Support Vector Machine (SVM) and Random Forest (RF) classification algorithms. Materials and Methods: Random Forest Classifier is applied on a Health dataset that consists of 304 records. A framework for heart disease prediction in the medical sector comparing Random Forest and SVM classifiers has been proposed and developed. The sample size was measured as 21 per group. The accuracy and the precision of the classifiers was evaluated and recorded. Results: The SVM classifier produces 53.04% in predicting the heart disease on the data set used whereas the Random forest classifier predicts the same at the rate of 83.2%. The significant value is 0.0. Hence RF is better than SVM. Conclusion: The performance of Random forest is better compared with SVM in terms of both precision and accuracy.

scholarly journals Hyperspectral LiDAR-Based Plant Spectral Profiles Acquisition: Performance Assessment and Results Analysis

2021 ◽  
Vol 13 (13) ◽  
pp. 2521
Author(s):  
Jianxin Jia ◽  
Changhui Jiang ◽  
Wei Li ◽  
Haohao Wu ◽  
Yuwei Chen ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Incident Angle ◽  
Vegetation Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Spectral Parameters ◽  
Lighting Condition ◽  
Wide Range ◽  
Spectral Profiles ◽  
Angle Effect

In precision agriculture, efficient fertilization is one of the most important pursued goals. Vegetation spectral profiles and the corresponding spectral parameters are usually employed for vegetation growth status indication, i.e., vegetation classification, bio-chemical content mapping, and efficient fertilization guiding. In view of the fact that the spectrometer works by relying on ambient lighting condition, hyperspectral/multi-spectral LiDAR (HSL/MSL) was invented to collect the spectral profiles actively. However, most of the HSL/MSL works with the wavelength specially selected for specific applications. For precision agriculture applications, a more feasible HSL capable of collecting spectral profiles at wide-range spectral wavelength is necessary to extract various spectral parameters. Inspired by this, in this paper, we developed a hyperspectral LiDAR (HSL) with 10 nm spectral resolution covering 500~1000 nm. Different vegetation leaf samples were scanned by the HSL, and it was comprehensively assessed for wide-range wavelength spectral profiles acquirement, spectral parameters extraction, vegetation classification, and the laser incident angle effect. Specifically, three experiments were carried out: (1) spectral profiles results were compared with that from a SVC spectrometer (HR-1024, Spectra Vista Corporation); (2) the extracted spectral parameters from the HSL were assessed, and they were employed as the input features of a support vector machine (SVM) classifier with multiple labels to classify the vegetation; (3) in view of the influence of the laser incident angle on the HSL reflected laser intensities, we analyzed the laser incident angle effect on the spectral parameters values. The experimental results demonstrated the developed HSL was more feasible for acquiring spectral profiles with wide-range wavelength, and spectral parameters and vegetation classification results also indicated its great potentials in precision agriculture application.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

Identification of Chronic Hypersensitivity Pneumonitis Biomarkers with Machine Learning and Differential Co-expression Analysis

2020 ◽  
Vol 20 ◽  
Author(s):  
Hongwei Zhang ◽  
Steven Wang ◽  
Tao Huang
Keyword(s):  
Feature Selection ◽  
Expression Analysis ◽  
Hypersensitivity Pneumonitis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Great Promise ◽  
Support Vector ◽  
Svm Classifier ◽  
Clinical Tool ◽  
Chronic Hypersensitivity Pneumonitis

Aims: We would like to identify the biomarkers for chronic hypersensitivity pneumonitis (CHP) and facilitate the precise gene therapy of CHP. Background: Chronic hypersensitivity pneumonitis (CHP) is an interstitial lung disease caused by hypersensitive reactions to inhaled antigens. Clinically, the tasks of differentiating between CHP and other interstitial lungs diseases, especially idiopathic pulmonary fibrosis (IPF), were challenging. Objective: In this study, we analyzed the public available gene expression profile of 82 CHP patients, 103 IPF patients, and 103 control samples to identify the CHP biomarkers. Method: The CHP biomarkers were selected with advanced feature selection methods: Monte Carlo Feature Selection (MCFS) and Incremental Feature Selection (IFS). A Support Vector Machine (SVM) classifier was built. Then, we analyzed these CHP biomarkers through functional enrichment analysis and differential co-expression analysis. Result: There were 674 identified CHP biomarkers. The co-expression network of these biomarkers in CHP included more negative regulations and the network structure of CHP was quite different from the network of IPF and control. Conclusion: The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF. Many of the biomarker genes on the differential co-expression network showed great promise in revealing the underlying mechanisms of CHP.

Sign in / Sign up

Export Citation Format

Share Document