Invention to increase cotton yield with artificial intelligence patented

The software named ‘Field Crop Yield Detection Method’ based on artificial intelligence, aiming to provide sustainable productivity in cotton, in which Yaşar University and May-Agro Seed Industry and Trade Co. Inc. are shareholders, has been registered by the Turkish Patent and Trademark Office. Professor Dr. Mehmet Süleyman Ünlütürk from the Department of Software Engineering at Yaşar University, Professor Dr. Murat Komesli from the Department of Management Information Systems at Yaşar University, and Research and Development Engineer Dr. Aslı Keçeli from May-Agro Seed Industry and Trade Co. Inc. have signed the invention, which increases productivity in cotton with artificial intelligence while decreasing production costs and risks. The ‘Field Crop Yield Detection Method,’ of which Yaşar University and May-Agro Seed Industry and Trade Co. Inc. are joint applicants, aims to predict the yield potential of cotton varieties, conduct breeding studies, and provide sustainable productivity. Professor Dr. Mehmet Ünlütürk from the Department of Software Engineering at Yaşar University, who made evaluations about the artificial intelligence-based software that will greatly contribute to the agricultural sector nationally and internationally, stated: ‘The study reveals how UAVs and artificial intelligence technologies can be used in agricultural production and the advantages these technologies provide in predicting cotton yield compared to traditional methods. This innovative approach offers the opportunity to make faster, cost-effective, and accurate yield predictions, especially in cotton production.’ Professor Dr. Ünlütürk also emphasized the importance of digital transformation in agriculture, stating, ‘The use of artificial intelligence is crucial as part of the digital transformation in agriculture to demonstrate how the use of innovative technologies can optimize agricultural production processes.’ The 4-year work on the ‘Field Crop Yield Detection Method’ was highlighted by Professor Dr. Murat Komesli from the Department of Management Information Systems at Yaşar University, indicating that the method, which will pave the way for a new direction in the agricultural sector, was developed through the following process: ‘Cotton is a product with costly physical harvesting. Additionally, the analysis of yield and fiber quality performances is important for seed companies, and predicting the yield potential of cotton varieties is a critical stage in breeding studies. Traditional methods are both time-consuming and costly. Therefore, we aimed to develop automatic yield prediction systems using high-resolution field images, unmanned aerial vehicles (UAV), and artificial intelligence (AI) technologies. Field trials were conducted at the MAY Seed Research and Development Station in Torbalı, Izmir. UAVs collected colored images using the Mika Sense Red Edge camera. High-resolution images were processed to identify different characteristics of cotton plants. Artificial intelligence models that capture important features related to cotton yield were developed. These models were trained to predict cotton yield in grams. Artificial intelligence successfully captured important features related to cotton yield from images.’ Professor Dr. Ünlütürk pointed out that the combination of UAVs and artificial intelligence technologies has great potential in terms of yield prediction and management in agricultural applications, stating, ‘Turkey could be a pioneer in this area. The combination of artificial intelligence and digitalization in agriculture could provide us with a significant competitive advantage internationally. Because companies still prefer to use conventional methods. They are not yet using artificial intelligence.’