Optimized convolution neural network with ant colony algorithm for accurate plant disease detection - Bondre, Shweta V.; Yadav, Uma; Bondre, Vipin D.; Agrawal, Poorva (IAES International Journal of Artificial Intelligence (IJ-AI), 2025)
In India, agriculture is the primary source of income for half the people. Even in situations of fast population growth, agriculture supplies nourishment for all people. To provide food for the entire population, it is advised to detect plant diseases at an early stage. Plant leaf diseases are recognized using images of the affected leaves. Deep learning (DL) research seems to offer several opportunities for increased accuracy. Ant colony optimization with convolution-neural-network (ACO-CNN), a new deep learning technique for identifying and categorizing diseases, is presented in this article. Ant colony optimization (ACO) was used to examine the efficacy of disease diagnostics in plant leaves. The convolution neural network (CNN) classifier is used to remove texture, color, and leaf arrangement geometry from the input images. The ACO-CNN model outperformed the support vector machine (SVM) and CNN models in terms of precision, recall, and accuracy. CNN's rate is 81.6% as compared to SVM's 80% accuracy level. In the “ACO-CNN” approach, the F1-score, recall, and precision have higher rates as compared to other models, and the “F1-score” has the highest rate compared with other models since the ACO-CNN model has an accuracy rate of 91.00%.