Advanced EfficientNetB3 based CNN for Multi-Class Plant Leaf Disease Detection

In developing countries, agriculture plays an essential role in economic growth by providing food security, employment, and raw materials for industries. Diseases in plants, as in other agricultural groups, have a great impact on the reduction of global crop yields. This indicates a need for modern precision agriculture to perform their identification fast and accurately. Nowadays, Artificial Intelligence (AI) and deep-learning algorithms are used to detect diseases from leaves images. These approaches are significantly better than traditional methods. Nevertheless, visually similar diseases identification and high accuracy gains involving plenty of categories continue to be challenging. In this research, we used plant village dataset and applied an efficient and enhanced deep learning approach to classify 38 distinct plant leaf diseases. EfficientNetB3 based Convolutional Neural Network (ENBCNN) is selected as a generalizing feature extractor and produces a distinctive classification layer projecting fine patterns of disease with high discriminability to support differentiation. The results of the experiments showed great accuracy 99.7%, very stable learning curves, and reliable cross-validation effects. Our proposed method can accurately identify a wide spectrum of diseases. Therefore, developing this system, in practical way to implement it into mobile devices and increase the data to improve its usefulness are real conditions.