In the science of agriculture, automation helps to improve the country’s quality, economic growth, and productivity. The fruit and vegetable variety influences both the export market and quality assessment. The market value of vegetables and fruits is a key sensory feature, which affects consumer preference and choice. Although the process of sorting and grading can be performed manually, it is inaccurate, time-consuming, unreliable, subjective, hard, expensive, and easily influenced by the surroundings. Therefore, intelligent classification technique is necessary for vegetables and fruits, along with the system for defect detection. This research aims to detect external defects in vegetables and fruits-based on morphology, color, and texture. In this proposed work, the various algorithms proposed for quality inspection, including external fruit defects (i.e., RGB to L*a*b* color conversion and defective area calculation methods are used to recognize errors in both apple and orange) and vegetables (i.e., K-means cluster and defective area calculation methods are used to identify defective tomatoes from their color), several image techniques are used. The overall accuracy achieved in quality analysis and defect detection is 87% (apple: 83%; orange: 93%; and tomatoes: 83%) of defective fruits (apple and orange) and vegetables (tomatoes).