Artificial Intelligence-Based Drone System for Multiclass Plant Disease Detection Using an Improved Efficient Convolutional Neural Network - Albattah, Waleed; Javed, Ali; Nawaz, Marriam; Masood, Momina; Albahli, Saleh (Frontiers in Plant Science, 2022)
The role of agricultural development is very important in the economy of a country. Whereas, the occurrence of several plant diseases is the major hindrance to the growth rate and quality of crops. The exact determination and categorization of crop leaf diseases is a complex and time-required activity due to the occurrence of low contrast information in the input samples. Moreover, the alterations in the size, location, structure of crop diseased portion, and existence of noise and blurriness effect in the input images further complicates the classification task. To solve the problems of existing techniques, a robust drone-based deep learning approach is proposed. More specifically, we have introduced an improved EfficientNetV2-B4 with additional added dense layers at the end of the architecture. The customized EfficientNetV2-B4 calculates the deep keypoints and classifies them in their related classes by utilizing an end-to-end training architecture. For performance evaluation, a standard dataset namely the PlantVillage Kaggle along with the samples captured by a drone is used which is complicated in the aspect of varying image samples with diverse image capturing conditions. We attain the average precision, recall, and accuracy values of 99.63%, 99.93%, and 99.99% respectively. The obtained results confirm the robustness of our approach in comparison to other latest techniques and show less time complexity as well.