Determination of methods for plant growth analysis with image processing and neural networks: the olive example

Image processing and artificial intelligence applications continue to develop every day to produce a series of solutions that will serve humanity. This thesis was conducted to serve the effective use of artificial intelligence applications, which are trained in a specific subject and improve themselves, in agricultural fields. Turkey ranks first in the world in terms of the number of olive trees, but the yield per tree is quite low. Nevertheless, despite ranking second in olive production after the European Union countries and being among the important olive regions of the world, olive agriculture in our country has not progressed at the desired level. The main reason for this situation is that advances in agricultural techniques and technology have not been sufficiently reflected in olive agriculture. In olive groves, precision agriculture methods and modern olive cultivation principles should be taken into consideration. A plant growth monitoring system was developed with image processing and neural networks and sampling was carried out on olive plants. In order to increase the production efficiency of the olive plant, there is a need to develop and implement plant growth monitoring systems with image processing and neural networks. In order to achieve this, an infrastructure is needed. In this study, real-time detection and classification of objects in the environment and the results obtained are presented. An olive plant is used in the experimental studies of the proposed method. The image of the olive plant in the study environment is captured with a camera and processed using image processing techniques. The size and area data of the olives on the image plane are calculated. By evaluating the obtained data, the olives are classified into classes in real time. Then, the measurement system is activated over the images belonging to the determined classes, and the infrastructure is created to create analyzes based on the measurements of the class.