An automatic method of detecting and analyzing trees from aerial images can help us in several ways, for example by keeping track of the number of trees that could be beneficial for the management of forest and other resources. Since a record of the number of trees in an area can stop deforestation, which is the most controversial issue for every country in the world, so a detailed study of tree counting and detection is more than necessary to effective management and quantitative analysis of the forest. In this study, we proposed an approach that can automatically segment regions with trees and estimate the number of trees in the input image. However, detecting an individual tree and counting it can be a difficult, and sometimes even inaccurate, task. This all depends on the conditions and the quality of the image taken. In this study, we propose and compare different approaches for detecting and counting trees in a given satellite image. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an original essay Our first approach is to apply morphological operations on the image to obtain a sharp and refined image. Marking local regional minima and maxima on a filtered image can help locate crown centroids and mark boundaries. Finally, watershed segmentation controlled by markers is applied on the image to separate two tree crowns in contact. Many areas contain spacing between trees, including small plants and shrubs, which contribute to the tree count, thus giving a false count of the number of trees in that area. To remove this ambiguity between small plants and trees, a color-based segmentation approach was developed to distinguish between plants and trees. The method based on HSV color space is well suited for this purpose, because HSV color removes all illumination in an image. After color conversion and enhancement, we can filter out small plants and shrubs with their respective hue values ​​compared to those of trees. Therefore, segmenting and applying watershed transformation now will give a more accurate count of trees in these regions. Nowadays, Deep Learning has gained popularity over time due to its ability to learn and analyze data much faster and more accurately, which is sometimes better. than any human being. Research has been conducted on many different general aerial images to automatically label aerial imagery with specific categories. In recent years, research and numerous algorithms have been developed and implemented for this sole purpose. Many of them include a machine learning and deep learning approach. The result of all this shows that deep learning is the best method compared to the satellite imagery dataset. Aerial imagery of the tree includes only the part of the crown of the tree which has many irregularities unlike the artificial structure such as buildings, roads which have a defined geometry and are easy to identify and classify. Keep in mind: This is just a sample.Get a custom article now from our expert writers.Get a custom essayIn order to classify an individual tree in a deep learning approach, we implement a neural network (CNN) for this task. THE..