Transfer Learning and UNet Segmentation for Paddy Leaf Disease Classification as a Solution with a User-Friendly Interface for Non-Technical Users

Paddy is a vital food crop that supports billions of people globally, and paddy cultivation is vital to the economic stability of numerous nations, acting as a key contributor to income and employment in agricultural communities, especially across Asia. Despite its importance, paddy cultivation is hindered by various leaf diseases such as Tungro, Sheath Blight (SB), Paddy Hispa (PH), Neck Blast (NB), Narrow Brown Spot (NBS), Leaf Scald (LS), Leaf Blast (LB), Brown Spot (BS), and Bacterial Leaf Blight (BLB), all of which negatively impact yield and grain quality. To address these issues, this study proposes a customized deep learning approach based on transfer learning. Six distinct models were evaluated, with the tailored DenseNet-121 model delivering the best performance, achieving an accuracy of 0.98, a precision of 0.97, and a recall of 0.96. To enhance model performance, image segmentation was performed using the UNet model, which significantly improved accuracy by creating a segmented image dataset. The six models were tested on two datasets: one containing segmented images and the other with non-segmented images, both derived from the Paddy Leaf Diseases Detection Dataset. Additionally, a simple and intuitive graphical interface was developed to allow users without technical backgrounds to conveniently interact with the model and identify paddy leaf diseases. This integrated solution highlights the effectiveness of deep learning in providing dependable and scalable methods for classifying paddy leaf diseases.