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Sajad Kiani

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Sajad Kiani
Ph.D Candidate
Department of Mechanics of Biosystems and Engineering
Tarbiat Modares University, Tehran
[email protected],
[email protected]

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Negin Azarabadi

kobra heidarbeigi

University of Tehran

Addí Rhode Navarro Cruz

ABDESSALEM TAHIRI

Mahdi Ghasemi-Varnamkhasti

Shahrekord University

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Papers by Sajad Kiani

Instrumental approaches and innovative systems for saffron quality assessment

Journal of Food Engineering, 2018

Saffron (Crocus sativus L.) in either unprocessed or distillate form is valuable in terms of its ... more Saffron (Crocus sativus L.) in either unprocessed or distillate form is valuable in terms of its color, flavor and medicinal properties. Due to its high economical value and demanding production, quality assessment of saffron as well as detection of adulterated products is of interest to the food industry and important to the consumers. A plethora of instruments and analytical methodologies have been already developed to determine not only the quality and quantity of the main ingredients but also the type and level of adulterations in saffron. Apart from traditional techniques, biomimetic-based techniques have shown reliable candidates for saffron quality assurance and control. After briefly reviewing the principles of these methodologies and some of the more recent applications for saffron quality characterization, the paper discusses the advantages and limitations of the methods being employed. Also, this paper provides an overview of the more recent and possible innovative systems, as well as, their future perspectives.

Prediction of essential oil content in spearmint (Mentha spicata) via near-infrared hyperspectral imaging and chemometrics

Scientific Reports

Spearmint (Mentha spicata L.) is grown for its essential oil (EO), which find use in food, bevera... more Spearmint (Mentha spicata L.) is grown for its essential oil (EO), which find use in food, beverage, fragrance and other industries. The current study explores the ability of near infrared hyperspectral imaging (HSI) (935 to 1720 nm) to predict, in a rapid, nondestructive manner, the essential oil content of dried spearmint (0.2 to 2.6% EO). Spectral values of spearmint samples varied considerably with spatial coordinates, and so the use of averaging the spectral values of a surface scan was warranted. Data preprocessing was done with Multiplicative Scatter Correction (MSC) or Standard Normal Variate (SNV). Selection of spectral input variables was done with Least Absolute Shrinkage and Selection Operator (LASSO), Principal Component Analysis (PCA) or Partial Least Squares (PLS). Regression was executed with linear regression (LASSO, PLS regression, PCA regression), Support Vector Machine (SVM) regression, and Multilayer Perceptron (MLP). The best prediction of EO concentration was ...

Aroma modeling and quality evaluation of spearmint (Mentha spicata subsp. spicata) using electronic nose technology coupled with artificial intelligence algorithms

Journal of Applied Research on Medicinal and Aromatic Plants

Integration of computer vision and electronic nose as non-destructive systems for saffron adulteration detection

Computers and Electronics in Agriculture, 2017

This work deals with the development and evaluation of an integrated system based on computer vis... more This work deals with the development and evaluation of an integrated system based on computer vision system (CVS) and electronic nose (e-nose) for saffron adulteration detection. Ten saffron samples adulterated with two common illegal constituents, namely, Artificially Colored Safflower (ACS) and Artificially Colored Yellow Styles of Saffron (ACYSS) at levels ranging from 10 to 50% (w/w) were characterized in this work. First, the developed CVS and e-nose system were integrated to form a unit system. This set up was utilized to extract color and aroma characteristic variables of each sample. The extracted variables were processed using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Support Vectors Machines (SVMs) to demonstrate the discrimination capability of the developed system. Two multilayer artificial neural network (ANN-MLP) models were also employed for saffron color and aroma strength prediction based on ISO standards. PCA and HCA results of the color and aroma datasets revealed that the adulterated samples have different color and aroma strength compared to authentic saffron and they can clearly be distinguished. SVMs classifier showed good agreement with the PCA results and reached 89% and 100% success rate in the recognition of the different saffron samples based on their color and aroma datasets, respectively. Results of the two ANN-MLP models proved that the developed system is capable of differentiating the authentic and adulterated saffron samples based on their color and aroma strength (R 2 Color analysis P 0:95 and R 2 Aroma analysis P 0:97).

A portable electronic nose as an expert system for aroma-based classification of saffron

Chemometrics and Intelligent Laboratory Systems, 2016

Abstract This study focuses on the development and evaluation of a portable electronic nose (e-no... more Abstract This study focuses on the development and evaluation of a portable electronic nose (e-nose) system for identification of different types of saffron, stigma of Crocus sativus L. (Iridaceae), based on their Volatile Organic Compounds (VOCs). The system utilizes metal oxide semiconductor gas sensors and direct head space sampling. Real-time data acquisition system, microcontroller devices and a laptop computer along with multivariate computational tools were used for development of an expert system. Eleven saffron samples from different regions were prepared for the experiments. Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) as unsupervised models and Multilayer Perceptron (MLP) neural networks and Partial Least Squares (PLS) as supervised models were utilized to develop the e-nose discrimination capability. Based on the results, PCA of volatile compounds fingerprints revealed eleven distinct groups corresponding to the eleven different saffron samples. This was further confirmed by HCA which classified the groups into five distinct Quality Classes (QCs) (excellent, very good, good, medium, and poor quality) which were used as the MLP and PLS classification goals. Results of analysis showed that performance of the MLP model for prediction of saffron samples QC was better than the PLS model, with 100% success rate and high correlation coefficients of cross validation (R 2 = 0.989 and relatively low RMSE value of 0.0141). These results show that the developed system is capable of discriminating saffron samples based on their aroma and can be utilized as an aroma quality control system.

Application of electronic nose systems for assessing quality of medicinal and aromatic plant products: A review

Journal of Applied Research on Medicinal and Aromatic Plants, 2016

Quality assurance has always been an important issue in the production and utilization of medicin... more Quality assurance has always been an important issue in the production and utilization of medicinal and aromatic plants. Most medicinal plants have aromatic characteristics due to the presence of Volatile Organic Compounds (VOCs) that can be analyzed using gas chromatography and mass spectroscopy. More modern techniques use sensors along with signal processing and advanced data processing algorithms. Electronic noses (e-noses), are instruments designed to mimic the sense of smell found in biological systems. E-noses contain numerous types of gas sensors that interact with odor molecules to produce electronic signals. Then, the sensor responses are sent to a computer system that uses multivariate data analysis methods to distinguish differences in the data based on the VOCs in the sample headspace. This paper provides a review of the applications of e-nose technologies for assessing the quality and safety of commercially-available medicinal plant products. Advantages and limitations of these systems are also covered.

Fusion of artificial senses as a robust approach to food quality assessment

Journal of Food Engineering, 2016

Abstract Electronic nose (E-nose), Electronic tongue (E-tongue) and Computer Vision System (CVS) ... more Abstract Electronic nose (E-nose), Electronic tongue (E-tongue) and Computer Vision System (CVS) are three analytical systems which have been used separately in the food and pharmaceutical industries as quality evaluation methods. The E-nose and E-tongue are designed to mimic the mammalian olfactory and taste systems, both containing sensors that non-selectively interact with flavor molecules to produce some sort of electronic signals. The produced signals are related to the material quality or its main chemical components. The major aim in the use of CVS is to realize the mode of operation of human vision and is based on image analysis to extract some specific attributes as quality features. The extracted color and flavor features are analyzed in a computer using multivariate data analysis to recognize patterns in the data. Since these three systems are non-destructive, rapid, economic and consistent, their fusion can form a powerful and objective inspection tool able to out-perform the individual constituting techniques. The combined evaluation technique has a variety of applications replacing the systems that may not have sufficient performance individually for specific uses. Application of these three techniques are individually reviewed with emphasis on the fusion of the artificial senses. Spectroscopy methods which are also covered can be coupled with these artificial senses to enrich the extracted information from the food industry.

Machine Vision and Soil Trace-based Guidance-Assistance System for Farm Tractors in Soil Preparation Operations

Journal of Agricultural Science, 2012

The main objective of this study was to develop a guidance-assistance system for agricultural veh... more The main objective of this study was to develop a guidance-assistance system for agricultural vehicles in land preparation and tilling operations. The proposed system is a potential replacement for markers on the implement toolbar. The new automatic steering system controls the vehicle path based on the trace of the previous tilling operation on the field. Hough transform method was adapted to process forward view images of the tractor captured by a digital camera. For traces coinciding with center line of the images no correction is necessary, otherwise the relative position of the trace with respect to image center line would be an indication of the need for steering wheel adjustment. The proposed system was evaluated at various conditions including three illumination levels, previous plant residue levels (9, 24 and 50%) and soil surfaces tilled by one of the three implements (plow, subsoiler and row-crop plant furrow opener). For each test accuracy of algorithm was determined and Line Prediction Error (LPE) was calculated by the algorithm. Tests were conducted in field at three forward speeds; 4, 7.5 and 11 km h-1 and three steering angles; 10 o , 16 o and 20.5 o. Distance error (DE) and response time (RT) for each forward speed and steering angle was measured and compared. Results indicated that various illumination and residue levels have not distorted the output of the system proposed (LPE was limited to ±1.72 cm). Response time was decreased for increasing forward speed and steering angle. Effect of forward speed on DE was significant whereas effect of steering angle on DE was not significant (0.05p).

Modeling of Perforated Sill-Controlled Hydraulic Jump

International Journal of Civil Engineering, 2017

Sequent depth of free hydraulic jump (m) Y Forced jump sequent depth ratio (−) Y * Free jump sequ... more Sequent depth of free hydraulic jump (m) Y Forced jump sequent depth ratio (−) Y * Free jump sequent depth ratio (−) Y s Difference in ratio of depths for sill-controlled and free jump (−) , The coefficients depend on the type of hydraulic jump (−) μ Water viscosity (kg m −2 s) ρ Water density (kg m −3

Crop Detection and Positioning in the Field Using Discriminant Analysis and Neural Networks Based on Shape Features

Journal of Agricultural Science and Technology, 2012

Development of an autonomous weeding machine requires a vision system capable of detecting and lo... more Development of an autonomous weeding machine requires a vision system capable of detecting and locating the position of the crop. It is important for the vision system to be able to recognize the accurate position of the crop stem to be protected during weeding. Several shape features of corn plants and common weed species in the location were extracted by means of morphological operations. Effective features in the classification of corn and weeds were analyzed using stepwise discriminant analysis. Among the seven features used in the analysis, four were sufficient to classify the two target groups of weeds and corn. These shape features were fed to artificial neural networks to discriminate between the weeds and the main crop. 180 images consisting of corn plants and four species of common weeds were collected from normal conditions of the field. Results showed that this technique was able to distinguish corn plants with an accuracy of 100% while at most 4% of the weeds were incor...

Discriminating The Corn Plants From The Weeds By Using Artificial Neural Networks

The main requisite for weeding-thinning machine is the location of the main stem of the crop. In ... more The main requisite for weeding-thinning machine is the location of the main stem of the crop. In this study, crops and their positions were detected using image processing techniques with the aid of artificial neural networks. Morphological operations were performed to singulate different objects in the images. Several shape features were fed to artificial neural network to discriminate between the weeds and the main crop. In the final stage, position of the crop was determined which is necessary for the weeding machine to root up all of the other plants. 196 images consisted of corn plants and four species of common weeds were collected from normal conditions of the field. Results showed that this technique was able to discriminate corn plants with an accuracy of 100%. It was concluded that high accuracy of this method is due to significant difference of corns and weeds in the critical period of weeding in the region.

Application of Co-occurrence Matrix on Wavelet Coefficients for Crop-weed Discrimination

The objective of this study was to discriminate the main crop from the common weeds in the field.... more The objective of this study was to discriminate the main crop from the common weeds in the field. Textural image analysis was applied to differentiate between the crop and the weeds. In the textural analysis, images were divided in square tiles, and were subjected to a wavelet transform. Wavelet transformation of digital images produces several spatial orientations which are highly effective in analyzing the information content of the images. In this study for each sub-band of wavelet coefficient, co-occurrence matrix was constructed to extract appropriate features for classification. Energy, entropy, contrast, homogeneity and inertia features were extracted from each orientation of the co-occurrence matrix. Finally, these features were fed into a multi-layer perceptron neural network to classify corn and four species of common weeds in the corn field. One hundred images were captured in normal condition of the plants in the field and were used to verify the ability of the proposed ...

Development and Evaluation of a Guidance-Assistant Algorithm for Agricultural Tractors in Plowing Operation

This paper describes a robust procedure to obtain a guidance directrix for agricultural tractors ... more This paper describes a robust procedure to obtain a guidance directrix for agricultural tractors in plowing operation with the wide toolbar. The automatic steering algorithm controls the vehicles based on the position of the trajectory results of previous operation on the field. Hough transform method was adapted to process forward view images of a vehicle captured by a digital camera installed at 1.1m height and angle of 35o respect to line of travel. The contrast between disturbed soil and undisturbed soil was the basis for detection of the last track of the machine. This algorithm was tested in the various situations including various illumination conditions and various plant residues. The position of the detected line respect to the furrow was measured by algorithm. The methodology devised to overcome the image noise problems and to be able to determine the proper trajectory for the tractor. The algorithm was able to find the trajectory with high accuracy. Maximum deviation of t...

Using artificial neural networks for discrimination of the corn plants from the weeds

The main requisite for weeding-thinning machine is the location of the main stem of the crop. In ... more The main requisite for weeding-thinning machine is the location of the main stem of the crop. In this study, crops and their positions were detected using image processing techniques with the aid of artificial neural networks. Morphological operations were performed to singulate different objects in the images. Several shape features were fed to artificial neural network to discriminate between the weeds and the main crop. In the final stage, position of the crop was determined which is necessary for the weeding machine to root up all of the other plants. 196 images consisted of corn plants and four species of common weeds were collected from normal conditions of the field. Results showed that this technique was able to discriminate corn plants with an accuracy of 100%. It was concluded that high accuracy of this method is due to significant difference of corns and weeds in the critical period of weeding in the region.

Computer Vision System Coupled with an Artificial Neural Network to Quality Evaluation of Rainbow Trout Eggs

1 MSc Graduated Student of Mechanics of Agricultural Machinery, Faculty of Agriculture, Shiraz Un... more 1 MSc Graduated Student of Mechanics of Agricultural Machinery, Faculty of Agriculture, Shiraz University, Fars, Iran. E-mail: [email protected]. 2 Ph.D Candidate of Biosystems Engineering Dep. Tarbiat Modares University, Tehran, Iran. * Corresponding authors; e-mails: [email protected]. 3 Coach of Mechanics of Agricultural Machinery, Faculty of Agriculture, University of Malayer, Ilam, Iran. E-mail: [email protected].

Automatic On-Line Depth Control of Seeding Units Using a Non-Contacting Ultrasonic Sensor

Previous investigations on seed placement depth emphasize the importance of proper seeding depth ... more Previous investigations on seed placement depth emphasize the importance of proper seeding depth for optimal crop stand and yield. On moist soils, care should be taken to plant as shallow as possible, but when surface soils are dry and sufficient moisture is not available at lower depths the planter should seed in a normal depth. To this effect, a non-contacting range sensing and control device for automatic control of seeding depth (DS) was developed. This system can measure the distance between the soil surface and the frame of a planter where sensor is mounted. In this system time needed for an acoustic pulse to travel to and from a transducer (fixed to tool-bar) to a reference surface (RS) (attached to planter furrow opener) is measured and forms a base for this technique. The reference surface plate (RS) is mounted on one side of the runner type furrow opener in such a way that as the runner increases its working depth the RS is pushed up to the soil surface. The sensor measure...

A.Jafari, “Crop Detection and Positioning in the field Using Discriminant Analysis and Neural Networks Based on shape features”, J-Agr.Sci.Tech

Development of an autonomous weeding machine requires a vision system capable of detecting and lo... more Development of an autonomous weeding machine requires a vision system capable of detecting and locating the position of the crop. It is important for the vision system to be able to recognize the accurate position of the crop stem to be protected during weeding. Several shape features of corn plants and common weed species in the location were extracted by means of morphological operations. Effective features in the classification of corn and weeds were analyzed using stepwise discriminant analysis. Among the seven features used in the analysis, four were sufficient to classify the two target groups of weeds and corn. These shape features were fed to artificial neural networks to discriminate between the weeds and the main crop. 180 images consisting of corn plants and four species of common weeds were collected from normal conditions of the field. Results showed that this technique was able to distinguish corn plants with an accuracy of 100% while at most 4% of the weeds were incorrectly classified as corn. In the final stage, the position of the main crop was also approximated and its accuracy was measured with respect to the real position of the crop. The position of the crop is necessary for the weeding machine to root up all of the plants except the main crop. It was concluded that the high accuracy of this method is due to the significant difference between corn and weeds in the critical period of weeding in the region.

PTR-QiToF-MS and HSI for the characterization of fermented cocoa beans from different origins

Food Chemistry

Hyperspectral imaging as a novel system for the authentication of spices: A nutmeg case study

LWT

Hyperspectral imaging, a non-destructive technique in medicinal and aromatic plant products industry: Current status and potential future applications

Computers and Electronics in Agriculture

Instrumental approaches and innovative systems for saffron quality assessment

Journal of Food Engineering, 2018

Saffron (Crocus sativus L.) in either unprocessed or distillate form is valuable in terms of its ... more Saffron (Crocus sativus L.) in either unprocessed or distillate form is valuable in terms of its color, flavor and medicinal properties. Due to its high economical value and demanding production, quality assessment of saffron as well as detection of adulterated products is of interest to the food industry and important to the consumers. A plethora of instruments and analytical methodologies have been already developed to determine not only the quality and quantity of the main ingredients but also the type and level of adulterations in saffron. Apart from traditional techniques, biomimetic-based techniques have shown reliable candidates for saffron quality assurance and control. After briefly reviewing the principles of these methodologies and some of the more recent applications for saffron quality characterization, the paper discusses the advantages and limitations of the methods being employed. Also, this paper provides an overview of the more recent and possible innovative systems, as well as, their future perspectives.

Prediction of essential oil content in spearmint (Mentha spicata) via near-infrared hyperspectral imaging and chemometrics

Scientific Reports

Spearmint (Mentha spicata L.) is grown for its essential oil (EO), which find use in food, bevera... more Spearmint (Mentha spicata L.) is grown for its essential oil (EO), which find use in food, beverage, fragrance and other industries. The current study explores the ability of near infrared hyperspectral imaging (HSI) (935 to 1720 nm) to predict, in a rapid, nondestructive manner, the essential oil content of dried spearmint (0.2 to 2.6% EO). Spectral values of spearmint samples varied considerably with spatial coordinates, and so the use of averaging the spectral values of a surface scan was warranted. Data preprocessing was done with Multiplicative Scatter Correction (MSC) or Standard Normal Variate (SNV). Selection of spectral input variables was done with Least Absolute Shrinkage and Selection Operator (LASSO), Principal Component Analysis (PCA) or Partial Least Squares (PLS). Regression was executed with linear regression (LASSO, PLS regression, PCA regression), Support Vector Machine (SVM) regression, and Multilayer Perceptron (MLP). The best prediction of EO concentration was ...

Aroma modeling and quality evaluation of spearmint (Mentha spicata subsp. spicata) using electronic nose technology coupled with artificial intelligence algorithms

Journal of Applied Research on Medicinal and Aromatic Plants

Integration of computer vision and electronic nose as non-destructive systems for saffron adulteration detection

Computers and Electronics in Agriculture, 2017

This work deals with the development and evaluation of an integrated system based on computer vis... more This work deals with the development and evaluation of an integrated system based on computer vision system (CVS) and electronic nose (e-nose) for saffron adulteration detection. Ten saffron samples adulterated with two common illegal constituents, namely, Artificially Colored Safflower (ACS) and Artificially Colored Yellow Styles of Saffron (ACYSS) at levels ranging from 10 to 50% (w/w) were characterized in this work. First, the developed CVS and e-nose system were integrated to form a unit system. This set up was utilized to extract color and aroma characteristic variables of each sample. The extracted variables were processed using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Support Vectors Machines (SVMs) to demonstrate the discrimination capability of the developed system. Two multilayer artificial neural network (ANN-MLP) models were also employed for saffron color and aroma strength prediction based on ISO standards. PCA and HCA results of the color and aroma datasets revealed that the adulterated samples have different color and aroma strength compared to authentic saffron and they can clearly be distinguished. SVMs classifier showed good agreement with the PCA results and reached 89% and 100% success rate in the recognition of the different saffron samples based on their color and aroma datasets, respectively. Results of the two ANN-MLP models proved that the developed system is capable of differentiating the authentic and adulterated saffron samples based on their color and aroma strength (R 2 Color analysis P 0:95 and R 2 Aroma analysis P 0:97).

A portable electronic nose as an expert system for aroma-based classification of saffron

Chemometrics and Intelligent Laboratory Systems, 2016

Abstract This study focuses on the development and evaluation of a portable electronic nose (e-no... more Abstract This study focuses on the development and evaluation of a portable electronic nose (e-nose) system for identification of different types of saffron, stigma of Crocus sativus L. (Iridaceae), based on their Volatile Organic Compounds (VOCs). The system utilizes metal oxide semiconductor gas sensors and direct head space sampling. Real-time data acquisition system, microcontroller devices and a laptop computer along with multivariate computational tools were used for development of an expert system. Eleven saffron samples from different regions were prepared for the experiments. Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) as unsupervised models and Multilayer Perceptron (MLP) neural networks and Partial Least Squares (PLS) as supervised models were utilized to develop the e-nose discrimination capability. Based on the results, PCA of volatile compounds fingerprints revealed eleven distinct groups corresponding to the eleven different saffron samples. This was further confirmed by HCA which classified the groups into five distinct Quality Classes (QCs) (excellent, very good, good, medium, and poor quality) which were used as the MLP and PLS classification goals. Results of analysis showed that performance of the MLP model for prediction of saffron samples QC was better than the PLS model, with 100% success rate and high correlation coefficients of cross validation (R 2 = 0.989 and relatively low RMSE value of 0.0141). These results show that the developed system is capable of discriminating saffron samples based on their aroma and can be utilized as an aroma quality control system.

Application of electronic nose systems for assessing quality of medicinal and aromatic plant products: A review

Journal of Applied Research on Medicinal and Aromatic Plants, 2016

Quality assurance has always been an important issue in the production and utilization of medicin... more Quality assurance has always been an important issue in the production and utilization of medicinal and aromatic plants. Most medicinal plants have aromatic characteristics due to the presence of Volatile Organic Compounds (VOCs) that can be analyzed using gas chromatography and mass spectroscopy. More modern techniques use sensors along with signal processing and advanced data processing algorithms. Electronic noses (e-noses), are instruments designed to mimic the sense of smell found in biological systems. E-noses contain numerous types of gas sensors that interact with odor molecules to produce electronic signals. Then, the sensor responses are sent to a computer system that uses multivariate data analysis methods to distinguish differences in the data based on the VOCs in the sample headspace. This paper provides a review of the applications of e-nose technologies for assessing the quality and safety of commercially-available medicinal plant products. Advantages and limitations of these systems are also covered.

Fusion of artificial senses as a robust approach to food quality assessment

Journal of Food Engineering, 2016

Abstract Electronic nose (E-nose), Electronic tongue (E-tongue) and Computer Vision System (CVS) ... more Abstract Electronic nose (E-nose), Electronic tongue (E-tongue) and Computer Vision System (CVS) are three analytical systems which have been used separately in the food and pharmaceutical industries as quality evaluation methods. The E-nose and E-tongue are designed to mimic the mammalian olfactory and taste systems, both containing sensors that non-selectively interact with flavor molecules to produce some sort of electronic signals. The produced signals are related to the material quality or its main chemical components. The major aim in the use of CVS is to realize the mode of operation of human vision and is based on image analysis to extract some specific attributes as quality features. The extracted color and flavor features are analyzed in a computer using multivariate data analysis to recognize patterns in the data. Since these three systems are non-destructive, rapid, economic and consistent, their fusion can form a powerful and objective inspection tool able to out-perform the individual constituting techniques. The combined evaluation technique has a variety of applications replacing the systems that may not have sufficient performance individually for specific uses. Application of these three techniques are individually reviewed with emphasis on the fusion of the artificial senses. Spectroscopy methods which are also covered can be coupled with these artificial senses to enrich the extracted information from the food industry.

Machine Vision and Soil Trace-based Guidance-Assistance System for Farm Tractors in Soil Preparation Operations

Journal of Agricultural Science, 2012

The main objective of this study was to develop a guidance-assistance system for agricultural veh... more The main objective of this study was to develop a guidance-assistance system for agricultural vehicles in land preparation and tilling operations. The proposed system is a potential replacement for markers on the implement toolbar. The new automatic steering system controls the vehicle path based on the trace of the previous tilling operation on the field. Hough transform method was adapted to process forward view images of the tractor captured by a digital camera. For traces coinciding with center line of the images no correction is necessary, otherwise the relative position of the trace with respect to image center line would be an indication of the need for steering wheel adjustment. The proposed system was evaluated at various conditions including three illumination levels, previous plant residue levels (9, 24 and 50%) and soil surfaces tilled by one of the three implements (plow, subsoiler and row-crop plant furrow opener). For each test accuracy of algorithm was determined and Line Prediction Error (LPE) was calculated by the algorithm. Tests were conducted in field at three forward speeds; 4, 7.5 and 11 km h-1 and three steering angles; 10 o , 16 o and 20.5 o. Distance error (DE) and response time (RT) for each forward speed and steering angle was measured and compared. Results indicated that various illumination and residue levels have not distorted the output of the system proposed (LPE was limited to ±1.72 cm). Response time was decreased for increasing forward speed and steering angle. Effect of forward speed on DE was significant whereas effect of steering angle on DE was not significant (0.05p).

Modeling of Perforated Sill-Controlled Hydraulic Jump

International Journal of Civil Engineering, 2017

Sequent depth of free hydraulic jump (m) Y Forced jump sequent depth ratio (−) Y * Free jump sequ... more Sequent depth of free hydraulic jump (m) Y Forced jump sequent depth ratio (−) Y * Free jump sequent depth ratio (−) Y s Difference in ratio of depths for sill-controlled and free jump (−) , The coefficients depend on the type of hydraulic jump (−) μ Water viscosity (kg m −2 s) ρ Water density (kg m −3

Crop Detection and Positioning in the Field Using Discriminant Analysis and Neural Networks Based on Shape Features

Journal of Agricultural Science and Technology, 2012

Development of an autonomous weeding machine requires a vision system capable of detecting and lo... more Development of an autonomous weeding machine requires a vision system capable of detecting and locating the position of the crop. It is important for the vision system to be able to recognize the accurate position of the crop stem to be protected during weeding. Several shape features of corn plants and common weed species in the location were extracted by means of morphological operations. Effective features in the classification of corn and weeds were analyzed using stepwise discriminant analysis. Among the seven features used in the analysis, four were sufficient to classify the two target groups of weeds and corn. These shape features were fed to artificial neural networks to discriminate between the weeds and the main crop. 180 images consisting of corn plants and four species of common weeds were collected from normal conditions of the field. Results showed that this technique was able to distinguish corn plants with an accuracy of 100% while at most 4% of the weeds were incor...

Discriminating The Corn Plants From The Weeds By Using Artificial Neural Networks

The main requisite for weeding-thinning machine is the location of the main stem of the crop. In ... more The main requisite for weeding-thinning machine is the location of the main stem of the crop. In this study, crops and their positions were detected using image processing techniques with the aid of artificial neural networks. Morphological operations were performed to singulate different objects in the images. Several shape features were fed to artificial neural network to discriminate between the weeds and the main crop. In the final stage, position of the crop was determined which is necessary for the weeding machine to root up all of the other plants. 196 images consisted of corn plants and four species of common weeds were collected from normal conditions of the field. Results showed that this technique was able to discriminate corn plants with an accuracy of 100%. It was concluded that high accuracy of this method is due to significant difference of corns and weeds in the critical period of weeding in the region.

Application of Co-occurrence Matrix on Wavelet Coefficients for Crop-weed Discrimination

The objective of this study was to discriminate the main crop from the common weeds in the field.... more The objective of this study was to discriminate the main crop from the common weeds in the field. Textural image analysis was applied to differentiate between the crop and the weeds. In the textural analysis, images were divided in square tiles, and were subjected to a wavelet transform. Wavelet transformation of digital images produces several spatial orientations which are highly effective in analyzing the information content of the images. In this study for each sub-band of wavelet coefficient, co-occurrence matrix was constructed to extract appropriate features for classification. Energy, entropy, contrast, homogeneity and inertia features were extracted from each orientation of the co-occurrence matrix. Finally, these features were fed into a multi-layer perceptron neural network to classify corn and four species of common weeds in the corn field. One hundred images were captured in normal condition of the plants in the field and were used to verify the ability of the proposed ...

Development and Evaluation of a Guidance-Assistant Algorithm for Agricultural Tractors in Plowing Operation

This paper describes a robust procedure to obtain a guidance directrix for agricultural tractors ... more This paper describes a robust procedure to obtain a guidance directrix for agricultural tractors in plowing operation with the wide toolbar. The automatic steering algorithm controls the vehicles based on the position of the trajectory results of previous operation on the field. Hough transform method was adapted to process forward view images of a vehicle captured by a digital camera installed at 1.1m height and angle of 35o respect to line of travel. The contrast between disturbed soil and undisturbed soil was the basis for detection of the last track of the machine. This algorithm was tested in the various situations including various illumination conditions and various plant residues. The position of the detected line respect to the furrow was measured by algorithm. The methodology devised to overcome the image noise problems and to be able to determine the proper trajectory for the tractor. The algorithm was able to find the trajectory with high accuracy. Maximum deviation of t...

Using artificial neural networks for discrimination of the corn plants from the weeds

The main requisite for weeding-thinning machine is the location of the main stem of the crop. In ... more The main requisite for weeding-thinning machine is the location of the main stem of the crop. In this study, crops and their positions were detected using image processing techniques with the aid of artificial neural networks. Morphological operations were performed to singulate different objects in the images. Several shape features were fed to artificial neural network to discriminate between the weeds and the main crop. In the final stage, position of the crop was determined which is necessary for the weeding machine to root up all of the other plants. 196 images consisted of corn plants and four species of common weeds were collected from normal conditions of the field. Results showed that this technique was able to discriminate corn plants with an accuracy of 100%. It was concluded that high accuracy of this method is due to significant difference of corns and weeds in the critical period of weeding in the region.

Computer Vision System Coupled with an Artificial Neural Network to Quality Evaluation of Rainbow Trout Eggs

1 MSc Graduated Student of Mechanics of Agricultural Machinery, Faculty of Agriculture, Shiraz Un... more 1 MSc Graduated Student of Mechanics of Agricultural Machinery, Faculty of Agriculture, Shiraz University, Fars, Iran. E-mail: [email protected]. 2 Ph.D Candidate of Biosystems Engineering Dep. Tarbiat Modares University, Tehran, Iran. * Corresponding authors; e-mails: [email protected]. 3 Coach of Mechanics of Agricultural Machinery, Faculty of Agriculture, University of Malayer, Ilam, Iran. E-mail: [email protected].

Automatic On-Line Depth Control of Seeding Units Using a Non-Contacting Ultrasonic Sensor

Previous investigations on seed placement depth emphasize the importance of proper seeding depth ... more Previous investigations on seed placement depth emphasize the importance of proper seeding depth for optimal crop stand and yield. On moist soils, care should be taken to plant as shallow as possible, but when surface soils are dry and sufficient moisture is not available at lower depths the planter should seed in a normal depth. To this effect, a non-contacting range sensing and control device for automatic control of seeding depth (DS) was developed. This system can measure the distance between the soil surface and the frame of a planter where sensor is mounted. In this system time needed for an acoustic pulse to travel to and from a transducer (fixed to tool-bar) to a reference surface (RS) (attached to planter furrow opener) is measured and forms a base for this technique. The reference surface plate (RS) is mounted on one side of the runner type furrow opener in such a way that as the runner increases its working depth the RS is pushed up to the soil surface. The sensor measure...

A.Jafari, “Crop Detection and Positioning in the field Using Discriminant Analysis and Neural Networks Based on shape features”, J-Agr.Sci.Tech

Development of an autonomous weeding machine requires a vision system capable of detecting and lo... more Development of an autonomous weeding machine requires a vision system capable of detecting and locating the position of the crop. It is important for the vision system to be able to recognize the accurate position of the crop stem to be protected during weeding. Several shape features of corn plants and common weed species in the location were extracted by means of morphological operations. Effective features in the classification of corn and weeds were analyzed using stepwise discriminant analysis. Among the seven features used in the analysis, four were sufficient to classify the two target groups of weeds and corn. These shape features were fed to artificial neural networks to discriminate between the weeds and the main crop. 180 images consisting of corn plants and four species of common weeds were collected from normal conditions of the field. Results showed that this technique was able to distinguish corn plants with an accuracy of 100% while at most 4% of the weeds were incorrectly classified as corn. In the final stage, the position of the main crop was also approximated and its accuracy was measured with respect to the real position of the crop. The position of the crop is necessary for the weeding machine to root up all of the plants except the main crop. It was concluded that the high accuracy of this method is due to the significant difference between corn and weeds in the critical period of weeding in the region.

PTR-QiToF-MS and HSI for the characterization of fermented cocoa beans from different origins

Food Chemistry

Hyperspectral imaging as a novel system for the authentication of spices: A nutmeg case study

LWT

Hyperspectral imaging, a non-destructive technique in medicinal and aromatic plant products industry: Current status and potential future applications

Computers and Electronics in Agriculture