Authors - Krunal Maheriya, Mrugendra Rahevar, Martin Parmar, Deep Kothadiya, Arpita Shah Abstract - Plant diseases pose a significant threat to agricultural output, causing food insecurity and economic losses. Early detection is crucial for effective treatment and control. Traditional diagnosis methods are labor intensive, time-consuming, and require specialized knowledge, making them unsuitable for large scale use. This study presents a novel approach for classifying cassava leaf diseases using stacked convolutional neural networks (CNNs). The proposed model leverages pre-trained ResNet-18 features to enhance feature learning and classification accuracy. The dataset includes images of cassava leaves with various diseases, such as Cassava Mosaic Disease (CMD), Cassava Green Mottle (CGM), Cassava Bacterial Blight (CBB), and Cassava Brown Streak Disease (CBSD). Our method begins with data preparation, including image augmentation to increase robustness and variability. The ResNet-18 model is then used to extract high-level features, which are then fed into a stacked CNN architecture made up of pooling layers, several convolutional layers, and non-linear activation functions. A fully connected layer is then used for classification. Experimental results demonstrate high accuracy in categorizing cassava leaf diseases. The proprietary stacked CNN architecture combined with pre-trained ResNet-18 features offers a significant improvement over conventional machine learning and image processing methods. This study advances precision agriculture by providing a scalable and effective method for early disease identification, enabling farmers to control diseases more accurately and promptly, thereby increasing crop yield. The findings point to the promise of deep learning techniques in agricultural applications and provide directions for further study to create more complex models for the classification and diagnosis of plant diseases.
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