AGRICULTURAL & ENVIRONMENTAL ENGINEERING
Permanent URI for this communityhttps://repository.ui.edu.ng/handle/123456789/567
Browse
2 results
Search Results
Item Automated fruit sorting system integrating image processing and support vector machine techniques(AccScience Publishing, 2025) Oyefeso, B. O.; Oyewande, O. E.; Audu, J.Traditional fruit grading methods are mostly time-consuming and subjective, thereby limiting efficiency in the agricultural sector. To address these problems, this paper presents the design and implementation of an automated fruit sorting system for classifying certain fruits, namely oranges, tomatoes, and mangoes, using image processing and support vector machine (SVM) techniques. An ESP32 camera was used to capture images of the fruits, which were later passed through algorithms in Python. Extracted features were then fed into a SVM model for the classification process of fruits. The model demonstrated excellent performance, achieving an accuracy of 100%, a precision of 96%, a recall of 92%, and an F1 score of 89%. The results indicated that incorporating multiple features significantly increases the accuracy of the classification. Moreover, the performance was optimized by selecting an appropriate regularization parameter during the training of the model and the use of polynomial kernel functions. Finally, the whole automated system was assembled to physically sort the classified fruits into different containers. This research highlights the potential of integrating image processing and machine learning technologies to revolutionize fruit classification processes, thereby improving both efficiency and quality control in agriculture.Item Development of an image processing algorithm for recognition of selected indigenous fruits(Nigerian Institution of Agricultural Engineers, 2023) Oyefeso, B. O.; Uduoka, J. T.Fruits are extremely fundamental in our everyday diet as they contain the vast majority of significant nutrients, minerals, and antioxidants. Sorting and grading of fruit are important aspects of its processing. However, these separation operations are still largely done manually in many developing countries including Nigeria. This study therefore, developed an algorithm for identifying and classifying fruit types. The proposed method involved the use of an image acquisition device, which acquired the images of the selected fruits namely apple, onion, banana, pepper and tomato fruits. These fruit images were divided into training and testing data sets. The algorithm extracted the textural and colour features of the fruit images from the training data sets to serve as templates for the testing procedure, after which they were processed using MATLAB software with Support Vector Machine (SVM) algorithm as the classifier. The fruit recognition system classified the input fruit sample by determining the similarities between the attributes (colour and Gray Level Co-occurrence Matrix values) of input fruit samples and the templates obtained from the training data sets. The levels of accuracy of the proposed system for apple, banana, green pepper, red pepper and tomato fruits were 97.2, 97.0, 97.2, 98.1 and 97.2%, respectively. The proposed method proved to be very promising in classifying the selected fruit types based on their colours and textural characteristics.