Remote sensing and image processing application to agriculture

Agriculture is one of the most important needs and livelihoods of human beings from past to present.Agriculture has always been in a technological change and development.Today, the agricultural sector is greatly affected by the rapid development of technology. Computer screening techniques such as deep learning methods are now widely used in agriculture. In apple orchards, when the fruits on the trees are ripe and ready for sale, the total yield in the field is estimated by experienced people and sales are averaged over these estimates. With this research, the images taken from the apple orchards were transferred to the computer environment and fruit yield was estimated with the help of artificial intelligence by using the numerical data obtained from deep learning techniques. With this way, it is aimed to make a more reliable trade by providing a healthy yield estimation with the help of artificial intelligence instead of the need for experienced people. In our thesis research, images of apple trees in ŞanlıurfaKüçükAkziyaret village were taken and transferred to the computer environment.The data required for apple yield estimation were coded using the Phyton programming language and using the Anaconda program and Spyder, the subprogram of the Anaconda program.OpenCV, Numpy libraries were used for coding.Object recognition will be made using the YOLOv3 artificial neural network and with the life of this information, the estimated amount of apples in the tree so yield, will be calculated.