Description of Invention
Agriculture plays a vital role in Malaysia’s economy. It is essential to ensure the proper growth and health of crops for the development of the agricultural sector. In the context of Malaysia, crop diseases pose a significant threat to agricultural output and, consequently, food security. This necessitates the timely and precise identification of such diseases to ensure the sustainability of food production. This study explores the application of deep learning techniques, specifically Ef-ficientNetB0, and machine learning algorithms, namely Support Vector Machines (SVM), for crop disease prediction. The proposed model is designed to classify diseases affecting three major crops: rice, potato, and corn. To achieve enhanced performance and accuracy, a hybrid model has been implemented. This model leverages EfficientNetB0's feature extraction capabilities, known for achieving rapid high learning rates, coupled with the classification proficiency of SVMs, a well-established machine learning algorithm. This hybrid approach addresses the challenges often encountered in precision agriculture applications, particularly those related to computational ef-ficiency. The proposed hybrid model achieved 97.29% accuracy. A comparative analysis with other models, CNN, VGG16, ResNet50, Xception, Mobilenet V2, Autoencoders, Inception v3 and Effi-cientNetB0 each achieving an accuracy of 86.57%, 83.29%, 68.79%, 94.07%, 90.71%, 87.90%, 94.14%, 96.14% respectively. Our proposed model achieved a better accuracy than the other models.