scholarly journals Early Detection of Broad-Leaved and Grass Weeds in Wide Row Crops Using Artificial Neural Networks and UAV Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 749
Author(s):  
Jorge Torres-Sánchez ◽  
Francisco Javier Mesas-Carrascosa ◽  
Francisco Jiménez-Brenes ◽  
Ana de Castro ◽  
Francisca López-Granados
Keyword(s):  
Neural Networks ◽  
Classification Problems ◽  
Weed Detection ◽  
Row Crops ◽  
Weed Mapping ◽  
Herbicide Treatment ◽  
Herbaceous Crops ◽  
Aerial Platforms ◽  
Grass Weed

Significant advances in weed mapping from unmanned aerial platforms have been achieved in recent years. The detection of weed location has made possible the generation of site specific weed treatments to reduce the use of herbicides according to weed cover maps. However, the characterization of weed infestations should not be limited to the location of weed stands, but should also be able to distinguish the types of weeds to allow the best possible choice of herbicide treatment to be applied. A first step in this direction should be the discrimination between broad-leaved (dicotyledonous) and grass (monocotyledonous) weeds. Considering the advances in weed detection based on images acquired by unmanned aerial vehicles, and the ability of neural networks to solve hard classification problems in remote sensing, these technologies have been merged in this study with the aim of exploring their potential for broadleaf and grass weed detection in wide-row herbaceous crops such as sunflower and cotton. Overall accuracies of around 80% were obtained in both crops, with user accuracy for broad-leaved and grass weeds around 75% and 65%, respectively. These results confirm the potential of the presented combination of technologies for improving the characterization of different weed infestations, which would allow the generation of timely and adequate herbicide treatment maps according to groups of weeds.

scholarly journals A Real-Time Weed Mapping and Precision Herbicide Spraying System for Row Crops

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4245 ◽  
Author(s):  
Yanlei Xu ◽  
Zongmei Gao ◽  
Lav Khot ◽  
Xiaotian Meng ◽  
Qin Zhang
Keyword(s):  
Real Time ◽  
Field Experiments ◽  
Particle Swarm ◽  
Error Rates ◽  
Integrated System ◽  
Central Processor ◽  
Time Data ◽  
Weed Detection ◽  
Row Crops ◽  
Weed Mapping

This study developed and field tested an automated weed mapping and variable-rate herbicide spraying (VRHS) system for row crops. Weed detection was performed through a machine vision sub-system that used a custom threshold segmentation method, an improved particle swarm optimum (IPSO) algorithm, capable of segmenting the field images. The VRHS system also used a lateral histogram-based algorithm for fast extraction of weed maps. This was the basis for determining real-time herbicide application rates. The central processor of the VRHS system had high logic operation capacity, compared to the conventional controller-based systems. Custom developed monitoring system allowed real-time visualization of the spraying system functionalities. Integrated system performance was then evaluated through field experiments. The IPSO successfully segmented weeds within corn crop at seedling growth stage and reduced segmentation error rates to 0.1% from 7.1% of traditional particle swarm optimization algorithm. IPSO processing speed was 0.026 s/frame. The weed detection to chemical actuation response time of integrated system was 1.562 s. Overall, VRHS system met the real-time data processing and actuation requirements for its use in practical weed management applications.

scholarly journals Characterization of III/V Semiconductors on Silicon by Analyzing 4D-STEM Data with Convolutional Neural Networks

2021 ◽  
Vol 27 (S1) ◽  
pp. 450-452
Author(s):  
Damien Heimes ◽  
Jonas Scheunert ◽  
Andreas Beyer ◽  
Jürgen Belz ◽  
Saleh Firoozabadi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks

scholarly journals Comparison of the efficiency of neural network algorithms in recognition and classification problems

2020 ◽  
Vol 224 ◽  
pp. 01025
Author(s):  
Alexey Beskopylny ◽  
Alexandr Lyapin ◽  
Nikita Beskopylny ◽  
Elena Kadomtseva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computation Time ◽  
Classification Problems ◽  
Handwritten Digit Recognition ◽  
Network Algorithms ◽  
Digit Recognition ◽  
Labour Intensity ◽  
Handwritten Digit ◽  
Accuracy Of Results

The article is devoted to the problem of comparing the effectiveness of feedforward (FF) and convolutional neural networks (CNN) algorithms in the problems of handwritten digit recognition and classification. In recent years, the attention of many researchers to the FF and CNN algorithms has given rise to many hybrid models focused on solving specific problems. At the same time, the efficiency of each algorithm in terms of accuracy and labour intensity remains unclear. It is shown that in classical problems, FFs can have advantages over CNN in terms of labour intensity with the same accuracy of results. Using the handwritten digits data from the MNIST database as an example, it is shown that FF algorithms provide greater accuracy and require less computation time than CNN.

scholarly journals PREDICTION OF FINANCIAL DISTRESS: CASE OF MINING ENTERPRISES IN CZECH REPUBLIC

2020 ◽  
Vol 15 (1) ◽  
pp. 1-14
Author(s):  
Zuzana Rowland ◽  
Alla Kasych ◽  
Petr Suler
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Czech Republic ◽  
Financial Stability ◽  
Grey Zone ◽  
Financial Health ◽  
Classification Problems ◽  
Mining Companies ◽  
Artificial Neural

The ability to predict a company's financial health is a challenge for many researchers and scientists. It is also a distracting topic, as many other new approaches to financial health predictions have emerged in recent years. In this paper, we focused on identifying the financial health of mining companies in the Czech Republic. We chose the neural network method because, based on various instances of related research, neural networks represent a more reliable financial forecast than mathematical-statistical methods such as discriminant analysis and logistic regression. The concept of a neural network emerged with the first artificial neural networks, inspired by biological systems. The existence of prediction and classification problems directly predetermines artificial neural networks with respect to a given issue. We used the Amadeus database for processing, including financial indicators, SPSS, and Visual Gene Developer software. In total, we analyzed sixty-four mining companies. Complete data on financial stability were available for fifty-three companies, which we explored, and based on these results, identified financial situations for the other thirteen. Based on the available information, we processed a neural network and regression analysis. We managed to classify thirteen companies as solvent, insolvent, and in the grey zone, with the help of prediction.

Convolutional Neural Networks for the Classification of the Microstructure of Tight Sandstone

2021 ◽  
Author(s):  
Ana Gabriela Reyna Flores ◽  
Quentin Fisher ◽  
Piroska Lorinczi
Keyword(s):  
Electron Microscopy ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Model Performance ◽  
Area Under The Curve ◽  
Tight Gas ◽  
Classification Problems ◽  
Tight Sandstone ◽  
Permeability Prediction ◽  
Rock Microstructure

Abstract Tight gas sandstone reservoirs vary widely in terms of rock type, depositional environment, mineralogy and petrophysical properties. For this reason, estimating their permeability is a challenge when core is not available because it is a property that cannot be measured directly from wire-line logs. The aim of this work is to create an automatic tool for rock microstructure classification as a first step for future permeability prediction. Permeability can be estimated from porosity measured using wire-line data such as derived from density-neutron tools. However, without additional information this is highly inaccurate because porosity-permeability relationships are controlled by the microstructure of samples and permeability can vary by over five orders of magnitude. Experts can broadly estimate porosity-permeability relationships by analysing the microstructure of rocks using Scanning Electron Microscopy (SEM) or optical microscopy. Such estimates are, however, subjective and require many years of experience. A Machine Learning model for the automation of rock microstructure determination on tight gas sandstones has been built using Convolutional Neural Networks (CNNs) and trained on backscattered images from cuttings. Current results were obtained by training the model on around 24,000 Back Scattering Electron Microscopy (BSEM) images from 25 different rock samples. The obtained model performance for the current dataset are 97% of average of both validation and test categorical accuracy. Also, loss of 0.09 and 0.089 were obtained for validation and test correspondingly. Such high accuracy and low loss indicate an overall great model performance. Other metrics and debugging techniques such Gradient-weighted Class Activation Mapping (Grad-CAM), Receiver Operator Characteristics (ROC) and Area Under the Curve (AUC) were considered for the model performance evaluation obtaining positive results. Nevertheless, this can be improved by obtaining images from new already available samples and make the model generalizes better. Current results indicate that CNNs are a powerful tool and their application over thin section images is an answer for image analysis and classification problems. The use of this classifier removes the subjectivity of estimating porosity-permeability relationships from microstructure and can be used by non-experts. The current results also open the possibility of a data driven permeability prediction based on rock microstructure and porosity from well logs.

Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN

2022 ◽  
pp. 166-201
Author(s):  
Asha Gowda Karegowda ◽  
Devika G.
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Parameter Optimization ◽  
Heuristic Algorithms ◽  
Search Space ◽  
Optimization Method ◽  
Classification Problems ◽  
Local Optima ◽  
Consistent Solution ◽  
Real Time Applications

Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters. This chapter will provide critics on both heuristics and meta-heuristic existing literature for training neural networks algorithms, applicability, and reliability on parameter optimization. In addition, the real-time applications of ANN will be presented. Finally, future directions to be explored in the field of ANN are presented which will of potential interest for upcoming researchers.

Confirmation and characterization of cyhalofop-butyl–resistant Chinese sprangletop (Leptochloa chinensis) populations from China

Weed Science ◽  
2020 ◽  
Vol 68 (3) ◽  
pp. 253-259
Author(s):  
Yajun Peng ◽  
Lang Pan ◽  
Ducai Liu ◽  
Xiaomei Cheng ◽  
Guolan Ma ◽  
...  
Keyword(s):  
Resistance Index ◽  
Resistance Mechanisms ◽  
The Other ◽  
Susceptible Population ◽  
Acetyl Coenzyme A ◽  
Target Site Resistance ◽  
High Level ◽  
Acetyl Coenzyme A Carboxylase ◽  
Grass Weed

AbstractChinese sprangletop [Leptochloa chinensis (L.) Nees] is one grass weed severely affecting rice (Oryza sativa L.) growth in paddies in China. Cyhalofop-butyl is the main herbicide used to control grass weeds in Chinese paddy fields, especially for controlling L. chinensis; however, L. chinensis has evolved resistance to cyhalofop-butyl due to continuous and extensive application. To investigate cyhalofop-butyl resistance levels and mechanisms in L. chinensis in some of the Chinese rice areas, 66 field populations were collected and treated with cyhalofop-butyl. Of these tested populations, 10 showed a high level of resistance to cyhalofop-butyl; the 50% effective dose ranged within 108.4 to 1,443.5 g ai ha−1 with resistance index values of 9.1 to 121.8 when compared with the susceptible population. Acetyl-coenzyme A carboxylase genes (ACCase) of susceptible and all 10 resistant populations were amplified and sequenced. Among them, Ile-1781-Leu, Trp-2027-Cys, Trp-2027-Ser, and Ile-2041-Asn mutations were found in five resistant populations. No known resistance-related mutations were found in the other five resistant populations, indicating that resistance to cyhalofop-butyl in these populations was likely to be endowed by non–target site resistance mechanisms. Notably, the Ile-1781-Leu and Trp-2027-Cys substitutions have previously been reported, but this is the first report of Trp-2027-Ser and Ile-2041-Asn mutations in L. chinensis. Furthermore, three derived cleaved amplified polymorphic sequence methods were developed to rapidly detect these mutations in L. chinensis.

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