• Ms. Chaithra M D

One of the most often used spices, black pepper has a distinct flavor that works well in both food and medicinal. In the agricultural industry, pepper grading is an essential procedure for guaranteeing uniform product quality and cost. Processed pepper berries are currently sorted by size, shape, and color by hand. This approach is labor-intensive, subjective, time-consuming, and error-prone because it mostly depends on the grader's experience. Using image processing and machine learning approaches, an automated pepper grading system can be created to get around these problems. Since the color of the peppers is a crucial indicator of their ripeness and quality, color feature extraction is a crucial step in the suggested procedure. Furthermore, the incorporation of CNN (Convolutional Neural Network) feature extraction enables the automatic learning and analysis of intricate properties, including texture, form, and other visual patterns. Through the integration of CNN's sophisticated capabilities with color feature extraction, the method improves pepper grading's precision, reliability, and effectiveness. This automated method decreases labor expenses, minimizes human mistake, and gives farmers and food processors insightful data to help them make better decisions.

Black pepper, Feature Extraction, Image Processing, Machine learning

"Black Pepper Grading System using Feature Extraction and Machine Learning Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 9, page no.e798-e803, September-2024, Available :http://www.jetir.org/papers/JETIR2409503.pdf

"Black Pepper Grading System using Feature Extraction and Machine Learning Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 9, page no. ppe798-e803, September-2024, Available at : http://www.jetir.org/papers/JETIR2409503.pdf