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ICT-Agri-Food Joint Call
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Gülüstan ÖKSÜZ
Turkey
Recep Tayyip Erdogan University
University
Climate-resilient tea varieties, Artificial intelligence, Sustainable farming, Climate change adaptation, Early and late harvests, Resilience breeding process, Data analysis methods, Machine learning techniques
TP 01: Use AI-systems (machine learning, deep learning and neural network approaches) to accelerate the digitisation in the agri-food sector
Project Title: Developing Climate-Resilient Tea Varieties through Artificial Intelligence-Assisted Breeding Methods to Ensure Sustainable Tea Farming
NOTE: We are extending an invitation to interested organizations to join us either as coordinators or partners in this endeavor.
Project Aim: The aim of the project is to make tea production sustainable while combating climate change and to increase resilience to adverse climate conditions in this field. Additionally, the aim is to increase the number of harvests and yield per unit area by developing tea varieties that can be harvested early and late.
In line with this, the three main objectives of our project are as follows:
Accelerating the Resilience Breeding Process: Utilizing new technologies and data analysis methods to shorten the lengthy breeding processes and make them more efficient; facilitating faster adaptation of tea plants to stress conditions such as drought, heat, and frost, and extending harvest periods with early and late tea varieties.
Providing Predictions for Breeding Studies: Artificial neural networks and machine learning techniques will be employed to analyze large-scale data sets and create models. These models will provide critical predictions for future breeding studies, contributing to scientific decision-making processes.
Popularization of Artificial Intelligence Applications in Agriculture: Our project aims to promote the use of artificial intelligence and machine learning technologies in the agriculture sector, speeding up the adoption of innovations in this field.
Key and Specific Activities to be Conducted under the Project:
Data Collection and Processing: Evaluation of 2034 genotypes in our Tea gene pool, which ranks 5th globally in terms of genotype diversity, will be carried out, and data obtained from our breeding studies will be collected and processed. These data will contain fundamental information for modeling.
Model Development: Various machine learning models will be employed including Multilayer Perceptron (MLP), Factor Regression (FR), Extreme Gradient Boosting (XGB), and Support Vector Machines (SVM). These techniques will be applied for data analysis and making predictions regarding breeding processes.
Model Validation and Application: The developed models will be tested and validated with real-world data. This step will ensure the accuracy and reliability of the models, enhancing the applicability of the project's results.
These activities will enable our project to achieve its objectives and facilitate significant progress in the tea farming sector.
NOTE: We are extending an invitation to interested organizations to join us either as coordinators or partners in this endeavor.
Project Aim: The aim of the project is to make tea production sustainable while combating climate change and to increase resilience to adverse climate conditions in this field. Additionally, the aim is to increase the number of harvests and yield per unit area by developing tea varieties that can be harvested early and late.
In line with this, the three main objectives of our project are as follows:
Accelerating the Resilience Breeding Process: Utilizing new technologies and data analysis methods to shorten the lengthy breeding processes and make them more efficient; facilitating faster adaptation of tea plants to stress conditions such as drought, heat, and frost, and extending harvest periods with early and late tea varieties.
Providing Predictions for Breeding Studies: Artificial neural networks and machine learning techniques will be employed to analyze large-scale data sets and create models. These models will provide critical predictions for future breeding studies, contributing to scientific decision-making processes.
Popularization of Artificial Intelligence Applications in Agriculture: Our project aims to promote the use of artificial intelligence and machine learning technologies in the agriculture sector, speeding up the adoption of innovations in this field.
Key and Specific Activities to be Conducted under the Project:
Data Collection and Processing: Evaluation of 2034 genotypes in our Tea gene pool, which ranks 5th globally in terms of genotype diversity, will be carried out, and data obtained from our breeding studies will be collected and processed. These data will contain fundamental information for modeling.
Model Development: Various machine learning models will be employed including Multilayer Perceptron (MLP), Factor Regression (FR), Extreme Gradient Boosting (XGB), and Support Vector Machines (SVM). These techniques will be applied for data analysis and making predictions regarding breeding processes.
Model Validation and Application: The developed models will be tested and validated with real-world data. This step will ensure the accuracy and reliability of the models, enhancing the applicability of the project's results.
These activities will enable our project to achieve its objectives and facilitate significant progress in the tea farming sector.
I am attaching CVs of some of the academics from our institution who could potentially be part of the project team. You can access the CVs through the following links:
Doc. Dr. Hatice Filiz Boyacı (https://avesis.erdogan.edu.tr/haticefiliz.boyaci)
Dr. Önder Albayrak (https://avesis.erdogan.edu.tr/onder.albayrak)
Dr. Kevser Çoğalmış (https://avesis.erdogan.edu.tr/kevser.cogalmis)
Doc. Dr. Murat Tören (https://avesis.erdogan.edu.tr/murat.toren)
Prof. Dr. Turan Yüksek (https://avesis.erdogan.edu.tr/turan.yuksek)
Doc. Dr. Hatice Filiz Boyacı (https://avesis.erdogan.edu.tr/haticefiliz.boyaci)
Dr. Önder Albayrak (https://avesis.erdogan.edu.tr/onder.albayrak)
Dr. Kevser Çoğalmış (https://avesis.erdogan.edu.tr/kevser.cogalmis)
Doc. Dr. Murat Tören (https://avesis.erdogan.edu.tr/murat.toren)
Prof. Dr. Turan Yüksek (https://avesis.erdogan.edu.tr/turan.yuksek)