Abstract
Detecting crop diseases before they spread poses a significant challenge for farmers. While both deep learning (DL) and computer vision are valuable for image classification, DL necessitates larger datasets and more extensive training periods. To overcome the limitations of working with constrained datasets, this paper proposes an ensemble model to enhance overall performance. The proposed ensemble model combines the convolution neural network (CNN)-based models as feature extractors with random forest (RF) as the output classifier. Our method is built on popular CNN-based models such as VGG16, InceptionV3, Xception, and ResNet50. Traditionally, these CNN-based architectures are referred to as one-way models, but in our approach, they are connected in parallel to form a two-way configuration, enabling the extraction of more diverse features and reducing the risk of underfitting, particularly with limited datasets. To demonstrate the effectiveness of our ensemble approach, we train models using the grape leaf dataset, which is divided into two subsets: original and modified. In the original set, background removal is applied to the images, while the modified set includes preprocessing techniques such as intensity averaging and bilateral filtering for noise reduction and image smoothing. Our findings reveal that ensemble models trained on modified images outperform those trained on the original dataset. We observe improvements of up to 5.6 % in accuracy, precision, and sensitivity, thus validating the effectiveness of our approach in enhancing disease pattern recognition within limited datasets.