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Globally, agriculture remains an important source of food and economic development. Due to various plant diseases, farmers continue to suffer huge yield losses in both quality and quantity. In this study, we explored the potential of using Artificial Neural Networks, K-Nearest Neighbors, Random Forest, and Support Vector Machine to classify tomato...
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... comparison of the results obtained in this study and other studies on the parameter of accuracy. The performance of the models was further analyzed by examining the confusion matrix to understand both successes and failures. This analysis helped identify the specific cases where the models excelled or became confused. Fig. 4, Fig. 5, Fig. 6, and Fig. 7 illustrate the correct and incorrect classification results of the models through confusion ...
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... of the total 735 samples for Helminthosporium. Thus, an accuracy of 93%. In Fig. 5, the KNN model correctly classified 635 samples out of the total 735 samples for Helminthosporium. Thus, an accuracy of 86.3%. In Fig. 6, the RF model correctly classified 604 samples out of the total 735 samples for Helminthosporium. Thus, an accuracy of 82.1%. In Fig. 7, the SVM model correctly classified 610 samples out of the total 735 samples for Curvularia. Thus, an accuracy of 82.9%. Generally, the confusion matrices imply that the models confused samples originally belonging to Helminthosporium with Curvularia, the classification boundary between these classes was not well learned by the ...
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... Thus, an accuracy of 82.9%. Generally, the confusion matrices imply that the models confused samples originally belonging to Helminthosporium with Curvularia, the classification boundary between these classes was not well learned by the classifiers. For example, in Fig. 6 62 images of Helminthosporium were classified as Curvularia, and in Fig. 7 59 images of Curvularia were classified as ...
Citations
Object detection in digital images has been implemented in various fields. Object detection faces challenges, one of which is rotation problems, causing objects to become unknown. We need a method that can extract features that do not affect rotation and reliable ensemble-based classification. The proposal uses the GLCM-MD (Gray-Level Co-occurrence Matrix Multi-Degrees) extraction method with classification using K-Nearest Neighbours (K-NN) and Random Forest (RF) learning as well as Voting Ensemble (VE) from two single classifications. The main goal is to overcome the difficulty of detecting objects when the object experiences rotation which results in significant visualization variations. In this research, the GLCM method is used to produce features that are stable against rotation. Furthermore, classification methods such as K-Nearest Neighbours (KNN), Random Forest (RF), and KNN-RF fusion using the Voting ensemble method are evaluated to improve detection accuracy. The experimental results show that the use of multi-degrees and the use of ensemble voting at all degrees can increase the accuracy value, and the highest accuracy for extraction using multi-degrees is 95.95%. Based on test results which show that the use of features of various degrees and the ensemble voting method can increase accuracy for detecting objects experiencing rotation
