Mustafa Ahmed Jalal

Verified @coagri.uobaghdad.edu.iq

University of Baghdad / College of Agricultural Engineering Sciences



              

https://researchid.co/mustafaa.jalal

EDUCATION

Magister

RESEARCH INTERESTS

Agriculture, Artificial Intelligence, Precision Agriculture, Agricultural Machinery

10

Scopus Publications

103

Scholar Citations

6

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • The use of image analysis to study the effect of moisture content on the physical properties of grains
    Łukasz Gierz, Mustafa Ahmed Jalal Al-Sammarraie, Osman Özbek, and Piotr Markowski
    Springer Science and Business Media LLC
    AbstractDesigning machines and equipment for post-harvest operations of agricultural products requires information about their physical properties. The aim of the work was to evaluate the possibility of introducing a new approach to predict the moisture content in bean and corn seeds based on measuring their dimensions using image analysis using artificial neural networks (ANN). Experimental tests were carried out at three levels of wet basis moisture content of seeds: 9, 13 and 17%. The analysis of the results showed a direct relationship between the wet basis moisture content and the main dimensions of the seeds. Based on the statistical analysis of the seed material, it was shown that the characteristics examined have a normal or close to normal distribution, and the seed material used in the investigation is representative. Furthermore, the use of artificial neural networks to predict the wet basis moisture content of seeds based on changes in their dimensions has an efficiency of 82%. The results obtained from the method used in this work are very promising for predicting the moisture content.

  • FRUIT CLASSIFICATION BY ASSESSING SLICE HARDNESS BASED ON RGB IMAGING. CASE STUDY: APPLE SLICES
    Bashar S. Falih, Łukasz Gierz, and Mustafa A.J. Al-Sammarraie
    Czestochowa University of Technology

  • Power Predicting for Power Take-Off Shaft of a Disc Maize Silage Harvester Using Machine Learning
    Mustafa A.J. Al-Sammarraie, Łukasz Adam Gierz, Osman Özbek, and Hasan Kırılmaz
    Wydawnictwo Naukowe Gabriel Borowski (WNGB)
    The relationship between the power consumed in the engine and the power take-off (P.T.O.) shaft of a maize silage harvester is critical to understanding the efficiency and performance of the harvester. The power consumed in the engine directly affects the power available for use on the P.T.O. shaft, which is the power source for the suspended silage harvesters. The research aimed to predict the power consumption of the P.T.O. shaft based on the power consumption of the tractor engine at different operating parameters, which are two applications of the P.T.O. shaft (540 and 540E rpm) and two forward speeds (1.8 and 2.5 km/h) using machine learning algorithms. The best re-sults in terms of engine power consumption were achieved in the 540E P.T.O. application, and the forward speed was 1.8 km/h. The results also gave a correlation between the power consumed by the engine and the P.T.O shaft of 87%. Regarding prediction algorithms, the Tree algorithm gave the highest prediction accuracy of 98.8%, while the KNN, SVM, and ANN algorithms gave an accuracy of 98.1, 60, and 60%, respectively.

  • Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times
    Sufyan A. Al-Mashhadany, Haider Ali Hasan, and Mustafa A. J. Al-Sammarraie
    Wydawnictwo Naukowe Gabriel Borowski (WNGB)
    The consumption of dried bananas has increased because they contain essential nutrients. In order to preserve bananas for a longer period, a drying process is carried out, which makes them a light snack that does not spoil quickly. On the other hand, machine learning algorithms can be used to predict the sweetness of dried bananas. The article aimed to study the effect of different drying times (6, 8, and 10 hours) using an air dryer on some physical and chemical characteristics of bananas, including CIE-L*a*b, water content, carbohydrates, and sweetness. Also predicting the sweetness of dried bananas based on the CIE-L*a*b ratios using machine learning algorithms RF, SVM, LDA, KNN, and CART. The results showed that increasing the drying time led to an increase in carbohydrates, sweetness, and CIE-L*a*b levels, while it led to a decrease in the moisture content in dried banana slices. Therefore, there is a direct relationship between CIE-L*a*b levels and sweetness. On the other hand, the RF and CART algorithms gave the highest prediction accuracy of 86% and 0.8 on the Kappa measure. While the other algorithms (SVM, LDA, KNN) gave a prediction accuracy of 80% and 0.7 on the Kappa measure. In terms of testing statistical significance, the null hypothesis (H0) was accepted because there is no relationship between the metric distributions of the algorithms used.

  • Effect of cold plasma technique on the quality of stored fruits-A case study on apples
    Ghaith H. Jihad, Mustafa A. J. Al-Sammarraie, and Firas Al-Aani
    FapUNIFESP (SciELO)
    ABSTRACT The consumption of fresh fruits has increased nowadays due to the lifestyle of the consumers. Maintaining the quality and nutritional value of cut fruits during storage is difficult compared to whole fruits. Deterioration of internal and external quality usually occurs in freshly harvested fruits. It is necessary to use different techniques to maintain the quality and increase the shelf life of the freshly cut product. This research studied the effect of treating apple slices with cold plasma once and with filtered water again on quality characteristics (hardness, moisture content, sugar content, carbohydrate content, and color) after being stored for five days. The best treatment was determined using two different pressures of the plasma jet (1 and 5 atm) and two different immersion times (3 and 6 minutes). It was verified the superiority of cold plasma treatment at 5 atm and 3 minutes immersion time in all studied traits, while treatment with filtered water and 6 minutes immersion time was superior concerning the moisture content of apple slices. There is an inverse relationship between L* and a direct relationship between the a* and b* values with the storage time. Therefore, the use of cold plasma treatment is promising in storing cut fruits, extending their shelf life, and improving their quality and safety, which provides fresh fruits.

  • Determine, Predict and Map Soil pH Level by Fiber Optic Sensor
    Mustafa Ahmed Jalal Al-Sammarraie, Firas Al-Aani, and Sufyan A. Al-Mashhadany
    IOP Publishing
    Abstract Soil pH is one of the main factors to consider before undertaking any agricultural operation. Methods for measuring soil pH vary, but all traditional methods require time, effort, and expertise. This study aimed to determine, predict, and map the spatial distribution of soil pH based on data taken from 50 sites using the Kriging geostatistical tool in ArcGIS as a first step. In the second step, the Support Vector Machines (SVM) machine learning algorithm was used to predict the soil pH based on the CIE-L*a*b values taken from the optical fiber sensor. The standard deviation of the soil pH values was 0.42, which indicates a more reliable measurement and the data distribution is normal. The Kriging method gave a prediction accuracy of 65% while the SVM algorithm gave an accuracy of 80%. The root mean square error (RMSE) was 0.36, 0.16 and the mean absolute error (MAE) was 0.37, 0.13, respectively, for the two methods. These two methods allow the prediction of soil pH and thus the assessment of soils, allowing for easier and more efficient management decisions and sustaining productivity.

  • Technological Advances in Soil Penetration Resistance Measurement and Prediction Algorithms
    Mustafa Ahmed Jalal Al-Sammarraie and Hasan Kırılmaz
    United Graduate School of Agricultural Science

  • Predicting Fruit’s Sweetness Using Artificial Intelligence—Case Study: Orange
    Mustafa Ahmed Jalal Al-Sammarraie, Łukasz Gierz, Krzysztof Przybył, Krzysztof Koszela, Marek Szychta, Jakub Brzykcy, and Hanna Maria Baranowska
    Applied Sciences (Switzerland) MDPI AG
    The manual classification of oranges according to their ripeness or flavor takes a long time; furthermore, the classification of ripeness or sweetness by the intensity of the fruit’s color is not uniform between fruit varieties. Sweetness and color are important factors in evaluating the fruits, the fruit’s color may affect the perception of its sweetness. This article aims to study the possibility of predicting the sweetness of orange fruits based on artificial intelligence technology by studying the relationship between the RGB values of orange fruits and the sweetness of those fruits by using the Orange data mining tool. The experiment has applied machine learning algorithms to an orange fruit image dataset and performed a comparative study of the algorithms in order to determine which algorithm has the highest prediction accuracy. The results showed that the value of the red color has a greater effect than the green and blue colors in predicting the sweetness of orange fruits, as there is a direct relationship between the value of the red color and the level of sweetness. In addition, the logistic regression model algorithm gave the highest degree of accuracy in predicting sweetness.


  • Comparison of the Effect Using Color Sensor and Pixy2 Camera on the Classification of Pepper Crop


RECENT SCHOLAR PUBLICATIONS

  • Challenges and innovations in potato harvester design: the role of artificial intelligence in improving crop sorting
    MAJ Al-Sammarraie, Z Gokalp, AI Ilbas
    Technology in Agronomy 5 (1) 2025

  • Harnessing automation techniques for supporting sustainability in agriculture
    MAJ Al-sammarraie, AI Ilbas
    Technology in Agronomy, 1-8 2024

  • FRUIT CLASSIFICATION BY ASSESSING SLICE HARDNESS BASED ON RGB IMAGING. CASE STUDY: APPLE SLICES.
    BS Falih, Ł Gierz, MAJ Al-Sammarraie
    Journal of Applied Mathematics & Computational Mechanics 23 (3) 2024

  • Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times.
    SA Al-Mashhadany, HA Hasan, MAJ Al-Sammarraie
    Journal of Ecological Engineering 25 (6) 2024

  • The use of image analysis to study the effect of moisture content on the physical properties of grains
    Ł Gierz, MAJ Al-Sammarraie, O zbek, P Markowski
    Scientific reports 14 (1), 11673 2024

  • Utilization Opportunities of Agricultural Biomass in Iraq
    B Demirel, MAJ Al-sammarraie, GAK Grdil, M Dağtekin
    Erciyes Tarım ve Hayvan Bilimleri Dergisi 7 (2) 2024

  • Power Predicting for Power Take-Off Shaft of a Disc Maize Silage Harvester Using Machine Learning
    MAJ Al-Sammarraie, Ł Gierz, O zbek, H Kırılmaz
    Advances in Science and Technology. Research Journal 18 (5) 2024

  • Effect of cold plasma technique on the quality of stored fruits-A case study on apples1
    GH Jihad, MAJ Al-Sammarraie, F Al-Aani
    Revista Brasileira de Engenharia Agrcola e Ambiental 28 (3), e276666 2024

  • Determine, Predict and Map Soil pH Level by Fiber Optic Sensor
    MAJ Al-Sammarraie, F Al-Aani, SA Al-Mashhadany
    IOP Conference Series: Earth and Environmental Science 1225 (1), 012104 2023

  • Technological Advances in Soil Penetration Resistance Measurement and Prediction Algorithms
    MAJ Al-Sammarraie, H Kırılmaz
    Reviews in Agricultural Science 11, 93-105 2023

  • Effective use of fertilizers and analysis of soil using precision agriculture techniques
    NA Jasim, OT Abdulmajeed, MA Jalal
    Iraqi Journal of Soil Science 22 (1), 157-164 2022

  • Predicting Fruit’s Sweetness Using Artificial Intelligence—Case Study: Orange
    MAJ Al-Sammarraie, Ł Gierz, K Przybył, K Koszela, M Szychta, J Brzykcy, ...
    Applied Sciences 12 (16), 8233 2022

  • The Effect of Twin-Row Silage Maize Harvesting on Single Row Silage Machine Performance
    M Al-Sammarraie, O ZBEK
    ANATOLIAN CONGRESSES 8TH INTERNATIONAL APPLIED SCIENCES CONGRESS/Turkey, 12-20 2021

  • DETERMINING THE EFFICIENCY OF A SMART SPRAYING ROBOT FOR CROP PROTECTION USING IMAGE PROCESSING TECHNOLOGY
    MAJ Al-Sammarraie, NA Jasim
    INMATEH - Agricultural Engineering 64 (2) 2021

  • New irrigation techniques for precision agriculture: a review
    MAJ Al-Sammarraie, AA Ali, NM Hussein
    Plant Archives 21 (1), 1734-1740 2021

  • The effect of plowing and pulverization systems on some plant indicators of onion
    JHN Al-Talabani, MAJ Al-Sammarraie
    Plant Arch 21, 851-853 2021

  • Determination of Grain Losses on Combine Harvester
    SA Mustafa AL-Sammarraie
    Journal of Scientific and Engineering Research 8 (1), 196-202 2021

  • Comparison of the Effect Using Color Sensor and Pixy2 Camera on the Classification of Pepper Crop
    MAJ Al-Sammarraie, O zbek
    Journal of Mechanical Engineering Research and Developments 44 (1), 396-403 2021

  • Determination of Operating Characteristics of 540 and 540E PTO Applications in Disc Type Silage Machines
    O zbek, MAJ Al-Sammarraie
    Turkish Journal of Agriculture-Food Science and Technology 8 (8), 1692-1696 2020

  • A precision use of thermostat in livestock monitoring system in a poultry house
    AA Ali, SA Rawdhan, MAJ Al-Sammarraie, GK Muhaibs
    iraqi poultry sciences journal 14 (1) 2020

MOST CITED SCHOLAR PUBLICATIONS

  • Predicting Fruit’s Sweetness Using Artificial Intelligence—Case Study: Orange
    MAJ Al-Sammarraie, Ł Gierz, K Przybył, K Koszela, M Szychta, J Brzykcy, ...
    Applied Sciences 12 (16), 8233 2022
    Citations: 28

  • Determine, Predict and Map Soil pH Level by Fiber Optic Sensor
    MAJ Al-Sammarraie, F Al-Aani, SA Al-Mashhadany
    IOP Conference Series: Earth and Environmental Science 1225 (1), 012104 2023
    Citations: 11

  • Comparison of the Effect Using Color Sensor and Pixy2 Camera on the Classification of Pepper Crop
    MAJ Al-Sammarraie, O zbek
    Journal of Mechanical Engineering Research and Developments 44 (1), 396-403 2021
    Citations: 11

  • DETERMINING THE EFFICIENCY OF A SMART SPRAYING ROBOT FOR CROP PROTECTION USING IMAGE PROCESSING TECHNOLOGY
    MAJ Al-Sammarraie, NA Jasim
    INMATEH - Agricultural Engineering 64 (2) 2021
    Citations: 7

  • Technological Advances in Soil Penetration Resistance Measurement and Prediction Algorithms
    MAJ Al-Sammarraie, H Kırılmaz
    Reviews in Agricultural Science 11, 93-105 2023
    Citations: 6

  • New irrigation techniques for precision agriculture: a review
    MAJ Al-Sammarraie, AA Ali, NM Hussein
    Plant Archives 21 (1), 1734-1740 2021
    Citations: 6

  • The use of image analysis to study the effect of moisture content on the physical properties of grains
    Ł Gierz, MAJ Al-Sammarraie, O zbek, P Markowski
    Scientific reports 14 (1), 11673 2024
    Citations: 5

  • Effect of cold plasma technique on the quality of stored fruits-A case study on apples1
    GH Jihad, MAJ Al-Sammarraie, F Al-Aani
    Revista Brasileira de Engenharia Agrcola e Ambiental 28 (3), e276666 2024
    Citations: 5

  • The effect of plowing and pulverization systems on some plant indicators of onion
    JHN Al-Talabani, MAJ Al-Sammarraie
    Plant Arch 21, 851-853 2021
    Citations: 5

  • Determination of Grain Losses on Combine Harvester
    SA Mustafa AL-Sammarraie
    Journal of Scientific and Engineering Research 8 (1), 196-202 2021
    Citations: 5

  • Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times.
    SA Al-Mashhadany, HA Hasan, MAJ Al-Sammarraie
    Journal of Ecological Engineering 25 (6) 2024
    Citations: 4

  • Power Predicting for Power Take-Off Shaft of a Disc Maize Silage Harvester Using Machine Learning
    MAJ Al-Sammarraie, Ł Gierz, O zbek, H Kırılmaz
    Advances in Science and Technology. Research Journal 18 (5) 2024
    Citations: 3

  • Effective use of fertilizers and analysis of soil using precision agriculture techniques
    NA Jasim, OT Abdulmajeed, MA Jalal
    Iraqi Journal of Soil Science 22 (1), 157-164 2022
    Citations: 3

  • Determination of Operating Characteristics of 540 and 540E PTO Applications in Disc Type Silage Machines
    O zbek, MAJ Al-Sammarraie
    Turkish Journal of Agriculture-Food Science and Technology 8 (8), 1692-1696 2020
    Citations: 2

  • The Effect of Knife Clearance on the Machine Performance in Disc Type Silage Machines
    MA Al-Sammarraie, O zbek
    Selcuk Journal of Agriculture and Food Sciences 33 (2), 74-81 2019
    Citations: 2