This article presents a content-based image classification system to monitor the ripeness process of bell pepper (sweet pepper) via investigating and classifying the different maturity/ripeness stages. The proposed approach consists of three phases; namely pre-processing, feature extraction, and classification phases. Since the color of bell pepper surface is the most important characteristic to observe ripeness, this system uses colored histogram for classifying ripeness stage. It implements principal components analysis (PCA) along with support vector machine (SVM) algorithms for feature extraction and classification of ripeness stages, respectively. The datasets used for experiments were constructed based on real sample images for bell pepper at different stages, which were collected from farms in Minya city, Upper Egypt. Datasets of of total 175 images were used for both training and testing datasets. Training dataset is divided into 5 classes representing the different stages of bell pepper ripeness. Experimental results showed that the proposed classification approach has obtained ripeness classification accuracy of 93.89%, using One-against-One Multi-class SVM with linear kernel function and 10-fold cross-validation.

One of the prime factors in ensuring a consistent marketing of crops is product quality, and the process of
determining ripeness stages is a very important issue in the industry of (fruits and vegetables) production,
since ripeness is the main quality indicator from the customers’ perspective. To ensure optimum yield of
high quality products, an objective and accurate ripeness assessment of agricultural crops is important. This
chapter discusses the problem of determining different ripeness stages of tomato and presents a content-based
image classification approach to automate the ripeness assessment process of tomato via examining and
classifying the different ripeness stages as a solution for this problem. It introduces a survey about resent
research work related to monitoring and classification of maturity stages for fruits/vegetables and provides
the core concepts of color features, SVM, and PCA algorithms. Then it describes the proposed approach for
solving the problem of determining different ripeness stages of tomatoes. The proposed approach consists of
three phases, namely pre-processing, feature extraction, and classification phase. The classification process
depends totally on color features (colored histogram and color moments), since the surface color of a tomato
is the most important characteristic to observe ripeness. This approach uses Principal Components Analysis
(PCA) and Support Vector Machine (SVM) algorithms for feature extraction and classification, respectively.