An Analysis of Image Processing in Forestry and Agriculture Review

Numerous techniques have been demonstrated for computer technologies to increase agricultural output. One method that is becoming into a valuable tool for picture processing. In order to help academics and farmers improve agricultural practices, this study provides a brief assessment on the use of image processing in forestry and agriculture. Modern agricultural techniques, pesticides and herbicides, plant nutrition management and plant development monitoring benefit greatly from image processing techniques. A future potential for image processing in various agricultural business situations is highlighted in this research. Segmentation in this context refers to the division of pixels in a picture into plant and nonplant pixels. For subsequent plant analysis, like plant categorization (in another meaning: determining this plant was considered either a crop or maybe a weed. Based on this analysis, the successful work depends herbicide application in smart agricultural applications, In this process, excellence is crucial. Fragmentation is first focused on through survey and then image pre-processing is carefully examined. Here, at this stage, the backgrounds of the plants are segmented (i.e., the soil attachments are isolated on one side and the plant on the other). The main plant harvesting strategies are three of which are segmentation based on threshold, segmentation based on deep learning and segmentation based on color index. The focus of our review is on color index methods due to their abundance in the literature. Therefore, according to literature research completed in last decade, specifically starting in the year 2008 to 2021, a thorough evaluation of the Color index-based categorization approaches are shown. Finally, we list the difficulties and a few chances for new breakthroughs in this field.