Mustafa Ahmed Jalal

@uobaghdad.edu.iq

University of Baghdad / College of Agricultural Engineering Sciences

Mustafa Ahmed Jalal


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https://researchid.co/mustafaa.jalal

EDUCATION

Magister

RESEARCH INTERESTS

Agriculture, Artificial Intelligence, Precision Agriculture, Agricultural Machinery
21

Scopus Publications

221

Scholar Citations

10

Scholar h-index

10

Scholar i10-index

Scopus Publications

  • Predicting Bitter Orange (Citrus aurantium L.) Maturity by Machine Learning Based on Picking Force in Smart Picker
    Mustafa A. J. Al-Sammarraie, Osman Özbek, Zeki Gokalp, Łukasz Gierz
    Applied Fruit Science, 2026
  • Application of you only look once algorithms to crop production management using unmanned aerial vehicles and computer vision systems
    Amjed R. Al-Abbas, Łukasz A. Gierz, Bashar S. Falih, Mustafa A. J. Al-Sammarraie
    Advances in Science and Technology Research Journal, 2026
    Global date palm production is steadily increasing and adopting technologies such as unmanned aerial vehicles (UAVs) and deep learning can reduce costs, save time, and improve productivity.To address this issue, the authors have proposed an innovative approach that uses UAVs for high-resolution aerial imaging.These images, collected by the Department of Computer Engineering at Al-Salam University in Baghdad and the Institute of Machine Design, Faculty of Mechanical Engineering, Poznan University of Technology, support improved orchard management, palm counting, and yield estimation. Precise spraying and pollination are also facilitated and accelerated, reducing overall cultivation costs.The proposed methodology involves processing captured images and applying three versions of the you only look once (YOLO) object detection algorithm, v11, v12, and YOLO-NAS -to determine the most effective model.The YOLOv12 model achieved the highest mAP@50 at 99.12%, which validates its superior performance in this application.The main innovation is the integration of deep learning-based palm crown detection with UAV imagery, enabling automated and scalable monitoring of palm plantations.The proposed methodology enables rapid, cost-effective, and scalable palm tree enumeration and management.A mobile application based on the trained model is planned to support real-time palm detection, yield estimation, and resource optimisation for farmers and stakeholders.
  • Analysis of the effectiveness of natural treatments for preserving apricots and the YOLOv7 application for early damage detection
    Mustafa A. J. Al-Sammarraie, Zeki Gokalp, Samsuzana Abd Aziz
    Discover Food, 2025
    Maintaining the quality of apricot fruits during storage is not an easy task due to the changes in their physical and chemical properties, so it is necessary to use less expensive, easy to apply, environmentally friendly, and safer preservatives to maintain the nutritional value of apricot. The damage to some fruits during storage can be a source of infection, which leads to the damage of healthy fruits more quickly, which requires building an intelligent model to detect damaged fruits. The aim of the research is to study the effect of immersing apricots in lemon juice once and sugar-water solution again on the quality properties of apricots, including sweetness, color, hardness, and water content. On the other hand, the YOLOv7 algorithm was used to detect healthy fruits and damaged areas using a camera. The results showed that sweetness increased with increasing immersion time in sugar-water solution to reach 22.1 Brix, while it decreased with increasing immersion time in lemon juice to 19.12 Brix. Also, hardness increased with increasing immersion time in sugar-water solution to reach 3.7 kg/cm 2 . The water content of apricots decreased with increasing immersion time in different immersion media from 77.14 g to 73.93 g. In addition, CIE-L*a*b levels increased with increasing immersion time in different immersion media. For the performance indicators of the YOLOv7 algorithm, precision of 84.5%, recall of 87%, F1 of 0.77, and mAP@0.5 of 77.2 were obtained, respectively. Therefore, this study is expected to reduce the workload in post-harvest fruit processing and help in the rapid identification and detection of damaged fruits based on smart detection algorithms, thus improving sorting efficiency and reducing both waste and economic losses, which enhances smart agriculture technologies.
  • Classification of Apple Slices Treated by Atmospheric Plasma Jet for Post-harvest Processes Using Image Processing and Convolutional Neural Networks
    Mustafa A. J. Al-Sammarraie, Łukasz Gierz, Ghaith H. Jihad, Zeki Gokalp, Osman Özbek, Piotr Markowski
    Food and Bioprocess Technology, 2025
    Apple slice grading is useful in post-harvest operations for sorting, grading, packaging, labeling, processing, storage, transportation, and meeting market demand and consumer preferences. Proper grading of apple slices can help ensure the quality, safety, and marketability of the final products, contributing to the post-harvest operations of the overall success of the apple industry. The article aims to create a convolutional neural network (CNN) model to classify images of apple slices after immersing them in atmospheric plasma at two different pressures (1 and 5 atm) and two different immersion times (3 and again 6 min) once and in filtered water based on the hardness of the slices using the k-Nearest Neighbors (KNN), Tree, Support Vector Machine (SVM), and Artificial Neural Network (ANN) algorithms. The results showed an inverse relationship between the storage period and the hardness of the apple slices, with the average hardness values gradually decreasing from 4.33 (day 1) to 3.37 (day 5). Treatment with atmospheric plasma at a pressure of 5 atm and an immersion time of 3 min gave the best results for maintaining the hardness of the slices during the storage period, recording values of 4.85 (first day) and 3.68 (fifth day), outperforming other treatments. The average improvement rate was 23.09% over five consecutive days. Regarding the CNN algorithms, the ANN algorithm achieved the highest classification accuracy of 97%, while the Tree algorithm achieved the lowest accuracy of 88.7%. The KNN and SVM algorithms achieved classification accuracies of 94.7% and 95.1%, respectively. The study demonstrated the possibility of using a CNN to classify apple slices based on the degree of hardness. Furthermore, the application of atmospheric plasma at 5 atmospheres with a 3-min immersion improves the firmness of the apple slices by inhibiting degradative enzymes while preserving the cellular structure and tissue quality.
  • Power requirements for corn silage harvesters and application of precision agricultural techniques: a review
    Mustafa AL-SAMMARRAIE, Osman ÖZBEK, Hasan KIRILMAZ
    Journal of Central European Agriculture, 2025
    The energy requirements of corn silage harvesters and the application of precision agricultural techniques are essential for efficient and productive agricultural practices. The article aims to review previous studies on the energy requirements needed for different corn silage harvesting machines, and on the other hand, to present methods for measuring corn silage productivity directly in the field and monitoring it based on microcontrollers and artificial intelligence techniques. The process of making corn silage is done by cutting green fodder plants into small pieces, so special harvesters are used for this, called corn silage harvesters. The purpose of harvesting corn silage is to efficiently collect and store as many digestible nutrients as possible per unit of land area. The energy required to harvest corn silage is affected by many factors, including crop moisture, cutting lengths, particle size distribution, etc. This requires understanding the energy requirements of the harvesters used in the process. Using micro-sensors, the feed rate into corn silage harvesters is measured based on load cell data. This method helps in understanding the energy consumption and efficiency of the harvester during the feeding process, leading to more efficient and productive operations. On the other hand, artificial intelligence techniques are used to measure core size and cutting length to control machining parameters. We conclude from this review that precision agriculture techniques help farmers understand the efficiency of corn silage harvesters and know silage yield and quality, which helps them make informed decisions regarding energy use and thus obtain high productivity.
  • Precision agriculture strategies to reduce the impacts of soil degradation: A comprehensive review
    Mustafa A.J. Al-Sammarraie, Sema Kaplan
    Cab Reviews Perspectives in Agriculture Veterinary Science Nutrition and Natural Resources, 2025
    This review discusses precision agriculture techniques that help reduce the effects of soil degradation and improve soil health, based on an analysis of studies published in scientific databases such as Web of Science, Scopus, IEEE Xplore, Google Scholar, and ScienceDirect, with an emphasis on recent field research. The methodology included a qualitative analysis of case studies and application experiments in different areas to evaluate the impact of technologies such as controlled traffic farming (CTF), mechanized guidance (MG), precision fertilization (PF), precision irrigation (PI), conservation tillage (CT), and precision tillage (PT). Research results showed, CT to maintain soil structure and reduce organic matter loss increases soil fertility in the long run. MG systems increase the efficiency of agricultural resource use and reduce field congestion, which improves soil health. However, by reducing unnecessary movement of agricultural equipment, CTF reduces soil degradation. In addition, PI and PF provide nutrients and water to plants in a balanced manner, which improves plant health and reduces environmental pollution; this, in turn, reduces soil degradation.
  • The role of modern agricultural practices in enhancing soil erosion resistance
    Mustafa A.J. Al-Sammarraie, Sema Kaplan
    Cab Reviews Perspectives in Agriculture Veterinary Science Nutrition and Natural Resources, 2025
    This review focuses on conservation agriculture (CA) and its effects on increasing the soil’s resistance to erosion. CA involves minimum soil disturbance (minimum tillage/ no-till), diversified crop rotation, and maintenance of the soil cover to increase soil fertility and reduce erosion. CA reduces soil loss by up to 90% and water erosion by approximately 50 to 70% from runoff as it increases the health of the soil, yield of crops, and water-retention capacity of the soil by incorporating soil organic matter and promoting biodiversity. Crop rotation prevents the replenishment and depletion of soil nutrients by atmospheric fixation of nitrogen/biological nitrogen fixation. Controlled traffic farming (CTF) is a new strategy in which traveling by agricultural equipment is minimized to preserve the integrity of the land and soil, and compaction is reduced. It has advanced tools such as remote sensing (RS) to assess the erosion of soil and to evaluate the effectiveness of such practices in place. They enhance farmers’ capacity for better management of the resources and access to accurate information about the soil. Even though the previously discussed practices have numerous benefits, there are shortcomings and limitations to implementing CA practices for enhanced resistance of the soil to erosion. CA entails a special tool, the affordability of which may not be accessible to small-scale farmers or farmers in developing countries. In addition, the deployment of advanced methods such as RS requires investment in technology and infrastructure. Environmental issues of more weeds and pests where the appropriate pesticides are not used, also face CA, and the technological issue of implementing advanced methods in certain regions. Finally, the practice of CA entails providing training and education to the farmers on the correct use of new technologies and new approaches, which can be difficult in some regions with limited training materials. Finally, the review highlights the importance of implementing these practices to ensure agricultural and environmental sustainability because CA is an essential means of ensuring sustainable farm productivity and improvement in soil erosion resistance.
  • Challenges and innovations in potato harvester design: the role of artificial intelligence in improving crop sorting
    Mustafa A. J. Al-Sammarraie, Zeki Gokalp, Ali Irfan Ilbas
    Technology in Agronomy, 2025
    As population growth increases the demand for crops increases and their quality improves, and it becomes necessary to find innovative and modern solutions to enhance production. In this context, artificial intelligence plays a pivotal role in developing new technologies to improve crop sorting and increase agricultural yields. The present review discusses the main differences between manual and mechanical potato harvesting, explaining the advantages and disadvantages of each method. Manual harvesting is highlighted as a traditional method that allows for greater precision in handling the crop, but it requires more time and effort. In contrast, mechanical harvesting provides greater efficiency and speed in the process, but it may damage some tubers due to the design of the machines. The present study also reviews the challenges facing the design of potato harvesting machines, such as the characteristics of potatoes and soil, as these factors play a major role in the performance of the machines. The modern performance of harvesting machines shows significant progress in efficiency, which contributes to improving agricultural operations and reducing waste. The present review also discusses innovations in the field of potato harvesting, such as the use of advanced designs to increase productivity. Finally, it highlights the role of artificial intelligence in improving post-harvest potato sorting operations, which enhances the quality of the final product and reduces waste, making it an essential element in enhancing the agricultural supply chain.
  • From data to decision: How wearable plant sensors help improving proactive irrigation strategies and water use efficiency
    Mustafa A.J. Al-Sammarraie, Ali Irfan Ilbas, Zeki Gokalp
    Cab Reviews Perspectives in Agriculture Veterinary Science Nutrition and Natural Resources, 2025
    Wearable sensors are a revolutionary tool in agriculture because they collect accurate data on plant environmental conditions that affect plant growth in real-time. Moreover, this technology is crucial in increasing agricultural sustainability and productivity by improving irrigation strategies and water resource management. This review examines the role of wearable sensors in measuring plant water content, leaf and air humidity, stem flow, plant and air temperature, light, and soil moisture sensors. Wearable sensors are designed to monitor various plant physiological parameters in real-time. These data, obtained through wearable sensors, provide information on plant water use and physiology, making our agricultural choices more informed and accurate. Internet of Things (IoT) technologies can improve irrigation strategies and reduce water consumption by analyzing data from wearable sensors and adapting it to automate the irrigation system. The review also highlights the importance of using Artificial Intelligence (AI) to predict plant water needs accurately. This review concludes that wearable sensors provide accurate and real-time data on the stress state of plants and their surroundings, improving water management efficiency and agricultural production sustainability. These IOT and AI-enabled technologies are a crucial milestone toward smart and sustainable agriculture, which shows the importance of innovation in responding to enhanced climate threats.
  • Advanced Machine Learning Models for Banana Sweetness Classification
    Mustafa A. J. Al-Sammarraie, Bashar S. Falih, Noor A. Jasim, Huda N. Al-Ani, Łukasz Gierz
    Journal of Physics Conference Series, 2025
    It takes a lot of time to classify the banana slices by sweetness level using traditional methods. By assessing the quality of fruits more focus is placed on its sweetness as well as the color since they affect the taste. The reason for sorting banana slices by their sweetness is to estimate the ripeness of bananas using the sweetness and color values of the slices. This classifying system assists in establishing the degree of ripeness of bananas needed for processing and consumption. The purpose of this article is to compare the efficiency of the SVM-linear, SVM-polynomial, and LDA classification of the sweetness of banana slices by their LRV level. The result of the experiment showed that the highest accuracy of 96.66% was achieved by the SVM-polynomial algorithm, while the lowest 86.66% by LDA algorithm. The SVM-linear also has an accuracy of 90%. The study showed how machine learning algorithms can be used to classify banana slices according to their sweetness.
  • 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, Piotr Markowski
    Scientific Reports, 2024
  • Harnessing automation techniques for supporting sustainability in agriculture
    Mustafa A. J. Al-sammarraie, Ali Irfan Ilbas
    Technology in Agronomy, 2024
  • Effect of cold plasma technique on the quality of stored fruits-A case study on apples
    Ghaith H. Jihad, Mustafa A. J. Al-Sammarraie, Firas Al-Aani
    Revista Brasileira De Engenharia Agricola E Ambiental, 2024
  • FRUIT CLASSIFICATION BY ASSESSING SLICE HARDNESS BASED ON RGB IMAGING. CASE STUDY: APPLE SLICES
    Bashar S. Falih, Łukasz Gierz, Mustafa A.J. Al-Sammarraie
    Journal of Applied Mathematics and Computational Mechanics, 2024
  • Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times
    Sufyan A. Al-Mashhadany, Haider Ali Hasan, Mustafa A. J. Al-Sammarraie
    Journal of Ecological Engineering, 2024
  • 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, Hasan Kırılmaz
    Advances in Science and Technology Research Journal, 2024
  • Technological Advances in Soil Penetration Resistance Measurement and Prediction Algorithms
    Mustafa Ahmed Jalal Al-Sammarraie, Hasan Kırılmaz
    Reviews in Agricultural Science, 2023
  • Determine, Predict and Map Soil pH Level by Fiber Optic Sensor
    Mustafa Ahmed Jalal Al-Sammarraie, Firas Al-Aani, Sufyan A. Al-Mashhadany
    Iop Conference Series Earth and Environmental Science, 2023
  • 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, Hanna Maria Baranowska
    Applied Sciences Switzerland, 2022
  • Comparison of the Effect Using Color Sensor and Pixy2 Camera on the Classification of Pepper Crop
    Journal of Mechanical Engineering Research and Developments, 2021
  • DETERMINING THE EFFICIENCY OF A SMART SPRAYING ROBOT FOR CROP PROTECTION USING IMAGE PROCESSING TECHNOLOGY
    Mustafa Ahmed Jalal Al-Sammarraie, Noor Ahmed Jasim
    Inmateh Agricultural Engineering, 2021

RECENT SCHOLAR PUBLICATIONS

  • Predicting Bitter Orange ( Citrus aurantium L.) Maturity by Machine Learning Based on Picking Force in Smart Picker
    MAJ Al-Sammarraie, O Özbek, Z Gokalp, Ł Gierz
    Applied Fruit Science 68 (3), 135 , 2026
    2026
  • Application of you only look once algorithms to crop production management using unmanned aerial vehicles and computer vision systems
    AR Al-Abbas, ŁA Gierz, BS Falih, MAJ Al-Sammarraie
    Advances in Science and Technology. Research Journal 20 (1), 1-13 , 2026
    2026
  • From data to decision: How wearable plant sensors help improving proactive irrigation strategies and water use efficiency
    MAJ Al-Sammarraie, AI Ilbas, Z Gokalp
    CABI Reviews 20 (1), 0084 , 2025
    2025
  • Advanced Machine Learning Models for Banana Sweetness Classification
    MAJ Al-Sammarraie, BS Falih, NA Jasim, HN Al-Ani, Ł Gierz
    Journal of Physics: Conference Series 3153 (1), 012025 , 2025
    2025
  • The role of modern agricultural practices in enhancing soil erosion resistance
    MAJ Al-Sammarraie, S Kaplan
    CABI Reviews 20 (1), 0064 , 2025
    2025
  • Classification of Apple Slices Treated by Atmospheric Plasma Jet for Post-harvest Processes Using Image Processing and Convolutional Neural Networks
    MAJ Al-Sammarraie, Ł Gierz, GH Jihad, Z Gokalp, O Özbek, P Markowski
    Food and Bioprocess Technology 18 (10), 8453-8467 , 2025
    2025
    Citations: 2
  • APPLICATIONS OF AI BOTS IN SUPPORTING AND PUBLISHING AGRICULTURAL RESEARCH: A CASE STUDY OF CHATGPT AND DEEPSEEK
    MAJ Al-Sammarraiea, AI Ilbasb
    Tropical Agroecosystems 6 (2), 39-47 , 2025
    2025
  • Precision agriculture strategies to reduce the impacts of soil degradation: A comprehensive review
    MAJ Al-Sammarraie, S Kaplan
    CABI Reviews 20 (1), 0037 , 2025
    2025
    Citations: 3
  • Evaluating Tillage Quality under Varying Speed and Depth Using YOLOv7-Based Image Analysis
    MAJ Al-Sammarraie, SA Al-Mashhadany, HA Hasan, Ł Gierz, ...
    Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural … , 2025
    2025
  • Analysis of the effectiveness of natural treatments for preserving apricots and the YOLOv7 application for early damage detection
    MAJ Al-Sammarraie, Z Gokalp, SA Aziz
    Discover Food 5 (1), 139 , 2025
    2025
    Citations: 2
  • Power requirements for corn silage harvesters and application of precision agricultural techniques: a review
    M AL-SAMMARRAIE, O ÖZBEK, H KIRILMAZ
    Journal of Central European Agriculture 26 (2), 394-404 , 2025
    2025
  • 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
    2025
    Citations: 3
  • Harnessing automation techniques for supporting sustainability in agriculture
    MAJ Al-sammarraie, AI Ilbas
    Technology in Agronomy, 1-8 , 2024
    2024
    Citations: 7
  • 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
    2024
    Citations: 13
  • The use of image analysis to study the effect of moisture content on the physical properties of grains
    Ł Gierz, MAJ Al-Sammarraie, O Özbek, P Markowski
    Scientific reports 14 (1), 11673 , 2024
    2024
    Citations: 16
  • Utilization Opportunities of Agricultural Biomass in Iraq
    B Demirel, MAJ Al-sammarraie, GAK Gürdil, M Dağtekin
    Erciyes Tarım ve Hayvan Bilimleri Dergisi 8 (1), 108-113 , 2024
    2024
    Citations: 1
  • 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 and Computational Mechanics 23 (3) , 2024
    2024
    Citations: 5
  • Power Predicting for Power Take-Off Shaft of a Disc Maize Silage Harvester Using Machine Learning
    MAJ Al-Sammarraie, Ł Gierz, O Özbek, H Kırılmaz
    Advances in Science and Technology. Research Journal 18 (5) , 2024
    2024
    Citations: 8
  • 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 Agrícola e Ambiental 28 (3), e276666 , 2024
    2024
    Citations: 12
  • 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
    2023
    Citations: 18

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 (Switzerland) 12 (16), 8233 , 2022
    2022
    Citations: 50
  • 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
    2023
    Citations: 18
  • The use of image analysis to study the effect of moisture content on the physical properties of grains
    Ł Gierz, MAJ Al-Sammarraie, O Özbek, P Markowski
    Scientific reports 14 (1), 11673 , 2024
    2024
    Citations: 16
  • Technological Advances in Soil Penetration Resistance Measurement and Prediction Algorithms
    MAJ Al-Sammarraie, H Kırılmaz
    Reviews in Agricultural Science 11, 93-105 , 2023
    2023
    Citations: 15
  • 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
    2024
    Citations: 13
  • Comparison of the Effect Using Color Sensor and Pixy2 Camera on the Classification of Pepper Crop
    MAJ Al-Sammarraie, O Özbek
    Journal of Mechanical Engineering Research and Developments 44 (1), 396-403 , 2021
    2021
    Citations: 13
  • 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 Agrícola e Ambiental 28 (3), e276666 , 2024
    2024
    Citations: 12
  • New irrigation techniques for precision agriculture: a review
    MAJ Al-Sammarraie, AA Ali, NM Hussein
    Plant Archives 21 (1), 1734-1740 , 2021
    2021
    Citations: 12
  • 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
    2021
    Citations: 11
  • Determination of Grain Losses on Combine Harvester
    SA Mustafa AL-Sammarraie
    Journal of Scientific and Engineering Research 8 (1), 196-202 , 2021
    2021
    Citations: 11
  • Power Predicting for Power Take-Off Shaft of a Disc Maize Silage Harvester Using Machine Learning
    MAJ Al-Sammarraie, Ł Gierz, O Özbek, H Kırılmaz
    Advances in Science and Technology. Research Journal 18 (5) , 2024
    2024
    Citations: 8
  • Harnessing automation techniques for supporting sustainability in agriculture
    MAJ Al-sammarraie, AI Ilbas
    Technology in Agronomy, 1-8 , 2024
    2024
    Citations: 7
  • 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
    2021
    Citations: 7
  • 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
    2022
    Citations: 6
  • 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 and Computational Mechanics 23 (3) , 2024
    2024
    Citations: 5
  • Precision agriculture strategies to reduce the impacts of soil degradation: A comprehensive review
    MAJ Al-Sammarraie, S Kaplan
    CABI Reviews 20 (1), 0037 , 2025
    2025
    Citations: 3
  • 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
    2025
    Citations: 3
  • Determination of Operating Characteristics of 540 and 540E PTO Applications in Disc Type Silage Machines
    O Özbek, MAJ Al-Sammarraie
    Turkish Journal of Agriculture-Food Science and Technology 8 (8), 1692-1696 , 2020
    2020
    Citations: 3
  • The Effect of Knife Clearance on the Machine Performance in Disc Type Silage Machines
    MA Al-Sammarraie, O Özbek
    Selcuk Journal of Agriculture and Food Sciences 33 (2), 74-81 , 2019
    2019
    Citations: 3
  • Classification of Apple Slices Treated by Atmospheric Plasma Jet for Post-harvest Processes Using Image Processing and Convolutional Neural Networks
    MAJ Al-Sammarraie, Ł Gierz, GH Jihad, Z Gokalp, O Özbek, P Markowski
    Food and Bioprocess Technology 18 (10), 8453-8467 , 2025
    2025
    Citations: 2