Author:
Pablo Guerra Juan,Cuevas Francisco
Abstract
Agriculture plays a crucial role in human survival, necessitating the development of efficient methods for food production. This chapter reviews Digital Image Processing (DPI) methods that utilize various color models to segment elements like leaves, fruits, pests, and diseases, aiming to enhance agricultural crop production. Recent DPI research employs techniques such as image subtraction, binarization, color thresholding, statistics, and convolutional filtering to segment and identify crop elements with shared attributes. DPI algorithms have a broad impact on optimizing resources for increased food production through agriculture. This chapter provides an overview of DPI techniques and their applications in agricultural image segmentation, including methods for detecting fruit quality, pests, and plant nutritional status. The review’s contribution lies in the selection and analysis of highly cited articles, offering readers a current perspective on DPI’s application in agricultural processes.
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