scholarly journals Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms

2021 ◽  
Vol 7 (11) ◽  
pp. 241
Author(s):  
Abdellatif Moussaid ◽  
Sanaa El Fkihi ◽  
Yahya Zennayi
Keyword(s):  
Satellite Images ◽  
New Technologies ◽  
Learning Algorithms ◽  
Field Measurements ◽  
Machine Learning Algorithms ◽  
Essential Factor ◽  
Tree Crown ◽  
Citrus Orchard ◽  
Study Region ◽  
Smart Agriculture

Smart agriculture is a new concept that combines agriculture and new technologies to improve the yield’s quality and quantity as well as facilitate many tasks for farmers in managing orchards. An essential factor in smart agriculture is tree crown segmentation, which helps farmers automatically monitor their orchards and get information about each tree. However, one of the main problems, in this case, is when the trees are close to each other, which means that it would be difficult for the algorithm to delineate the crowns correctly. This paper used satellite images and machine learning algorithms to segment and classify trees in overlapping orchards. The data used are images from the Moroccan Mohammed VI satellite, and the study region is the OUARGHA citrus orchard located in Morocco. Our approach starts by segmenting the rows inside the parcel and finding all the trees there, getting their canopies, and classifying them by size. In general, the model inputs the parcel’s image and other field measurements to classify the trees into three classes: missing/weak, normal, or big. Finally, the results are visualized in a map containing all the trees with their classes. For the results, we obtained a score of 0.93 F-measure in rows segmentation. Additionally, several field comparisons were performed to validate the classification, dozens of trees were compared and the results were very good. This paper aims to help farmers to quickly and automatically classify trees by crown size, even if there are overlapping orchards, in order to easily monitor each tree’s health and understand the tree’s distribution in the field.

scholarly journals A Review on Various Algorithms used in Machine Learning

2019 ◽  
pp. 915-920
Author(s):  
Divya Chaudhary ◽  
Er. Richa Vasuja
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
New Technologies ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Training Set ◽  
Learning Data ◽  
Do So

In today's scenario all of data is being generated by everyone of us . so it becomes vital for us to handle this data. To do so new technologies are being developed such as machine learning, data mining etc. This paper gives the study related to machine learning(ML).Precise approximations are repetitively being produced by Machine Learning algorithms. Machine learning system effectively “learns” how to guess from training set of completed jobs. The main purpose of the review is to give a jagged estimate or overview about the mostly used algorithms in machine learning.

scholarly journals Evaluating essential features of proppant transport at engineering scales combining field measurements with machine learning algorithms

2021 ◽  
Author(s):  
Xiaoyu Wang ◽  
Lei Hou ◽  
Xueyu Geng ◽  
Peibin Gong ◽  
Honglei Liu
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Learning Algorithms ◽  
Field Measurements ◽  
Stratified Flow ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Proppant Transport ◽  
Control Variate ◽  
Injection Process

The characterization of the proppant transport at a field-engineering scale is still challenging due to the lack of direct subsurface measurements. Features that control the proppant transport may link the experimental and numerical observations to the practical operations at a field scale. To improve the numerical and laboratory simulations, we propose a machine-learning-based workflow to evaluate the essential features of proppant transport and their corresponding calculations. The proppant flow in fractures is estimated by applying the Gated recurrent unit (GRU) and Support-vector machine (SVM) algorithms to the measurements obtained from shale gas fracturing operations. Over 430,000 groups of fracturing data are collected and pre-processed by the proppant transport models to calculate key features, including settlement, stratified flow and inception of settled particles. The features are then fed into machine learning algorithms for pressure prediction. The root mean squared error (RMSE) is used as the criterion for ranking selected features via the control variate method. Our result shows that the stratified-flow feature (fracture-level) possesses better interpretations for the proppant transport, in which the Bi-power model helps to produce the best predictions. The settlement and inception features (particle-level) perform better in cases that the pressure fluctuates significantly, indicating that more complex fractures may have been generated. Moreover, our analyses on the remaining errors in the pressure-ascending cases suggest that (1) an introduction of the alternate-injection process, and (2) the improved calculation of proppant transport in complex fracture networks and highly-filled fractures will be beneficial to both experimental observations and field applications.

scholarly journals INDIVIDUAL BANANA TREE CROWN DELINEATION USING UNMANNED AERIAL VEHICLE (UAV) IMAGES

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 581-585
Author(s):  
S. Kuikel ◽  
B. Upadhyay ◽  
D. Aryal ◽  
S. Bista ◽  
B. Awasthi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Tree Crown ◽  
Individual Tree ◽  
Banana Tree ◽  
Crown Delineation ◽  
Uav Images

Abstract. Individual Tree Crown (ITC) delineation from aerial imageries plays an important role in forestry management and precision farming. Several conventional as well as machine learning and deep learning algorithms have been recently used in ITC detection purpose. In this paper, we present Convolutional Neural Network (CNN) and Support Vector Machine (SVM) as the deep learning and machine learning algorithms along with conventional methods of classification such as Object Based Image Analysis (OBIA) and Nearest Neighborhood (NN) classification for banana tree delineation. The comparison was done based by considering two cases; Firstly, every single classifier was compared by feeding the image with height information to see the effect of height in banana tree delineation. Secondly, individual classifiers were compared quantitatively and qualitatively based on five metrices i.e., Overall Accuracy, Recall, Precision, F-Score, and Intersection Over Union (IoU) and best classifier was determined. The result shows that there are no significant differences in the metrices when height information was fed as there were banana tree of almost similar height in the farm. The result as discussed in quantitative and qualitative analysis showed that the CNN algorithm out performed SVM, OBIA and NN techniques for crown delineation in term of performance measures.

scholarly journals Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1293
Author(s):  
Shamil Islamov ◽  
Alexey Grigoriev ◽  
Ilia Beloglazov ◽  
Sergey Savchenkov ◽  
Ove Tobias Gudmestad
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Oil And Gas ◽  
New Technologies ◽  
Learning Algorithms ◽  
Gas Production ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Drilling Process ◽  
Well Drilling

This article takes an approach to creating a machine learning model for the oil and gas industry. This task is dedicated to the most up-to-date issues of machine learning and artificial intelligence. One of the goals of this research was to build a model to predict the possible risks arising in the process of drilling wells. Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays, companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work describes a comparison of machine learning algorithms for anomaly detection during well drilling. In particular, machine learning algorithms will make it possible to make decisions when determining the geometry of the grid of wells—the nature of the relative position of production and injection wells at the production facility. Development systems are most often subdivided into the following: placement of wells along a symmetric grid, and placement of wells along a non-symmetric grid (mainly in rows). The tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we used historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. The experiential results illustrate that a model based on gradient boosting can classify the complications in the drilling process better than other models.

scholarly journals GEOLOGICAL MAPPING USING MACHINE LEARNING ALGORITHMS

2016 ◽  
Vol XLI-B8 ◽  
pp. 423-430 ◽  
Author(s):  
A. S. Harvey ◽  
G. Fotopoulos
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithms ◽  
Geological Mapping ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Calibration Data ◽  
Bare Rock ◽  
Study Region ◽  
Ground Validation

Remotely sensed spectral imagery, geophysical (magnetic and gravity), and geodetic (elevation) data are useful in a variety of Earth science applications such as environmental monitoring and mineral exploration. Using these data with Machine Learning Algorithms (MLA), which are widely used in image analysis and statistical pattern recognition applications, may enhance preliminary geological mapping and interpretation. This approach contributes towards a rapid and objective means of geological mapping in contrast to conventional field expedition techniques. In this study, four supervised MLAs (naïve Bayes, k-nearest neighbour, random forest, and support vector machines) are compared in order to assess their performance for correctly identifying geological rocktypes in an area with complete ground validation information. Geological maps of the Sudbury region are used for calibration and validation. Percent of correct classifications was used as indicators of performance. Results show that random forest is the best approach. As expected, MLA performance improves with more calibration clusters, i.e. a more uniform distribution of calibration data over the study region. Performance is generally low, though geological trends that correspond to a ground validation map are visualized. Low performance may be the result of poor spectral images of bare rock which can be covered by vegetation or water. The distribution of calibration clusters and MLA input parameters affect the performance of the MLAs. Generally, performance improves with more uniform sampling, though this increases required computational effort and time. With the achievable performance levels in this study, the technique is useful in identifying regions of interest and identifying general rocktype trends. In particular, phase I geological site investigations will benefit from this approach and lead to the selection of sites for advanced surveys.

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