Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3581 2019 2019 Research Scholar, Department of Computer Science,PSG College of Arts & Science, Coimbatore, Tamilnadu, India Shylaja Shylaja Associate professor & Dean, Department of Computer Science,PSG College of Arts & Science, Coimbatore, Tamilnadu, India Revathi Thiyagarajan Turmeric is a rhizomatous crop recognized for its medicinal effects which requires significant observation to ensure appropriate growth and progression. Turmeric plant diseases cause yield losses impacting food production systems and causing economic losses. Early prevention of these diseases is crucial for improving agricultural productivity. For this reason, The Improved YOLOV3-Tiny Model (IY3TM) was developed using Cycle-GAN and Convolutional Neural Network (CNN) with residual network for the early turmeric plant disease detection. However, this model leads to the omission of vital details along with the exact positioning of key attributes, thereby decreasing prediction accuracy. To resolve this, Convolutional and Vision Transformer model for Turmeric Diseases Detection (ConViT-TDD) is proposed for the prediction of turmeric plant diseases. ConViT-TDD is integrated into IY3TM with a self-attention mechanism and CNN-based global perspective to enhance the performance of the model A ConViT-TDD block involves the input channel transformation, the channel as well as spatial attention mechanism and global-minded transformers. The input channel transformation utilizes a convolutional layer to minimize the dimension of input channel and reduces the computational complexity. Global-minded transformers generate a feature vector based on the input channel transformation that is then transmitted to the encoder component. By collecting channel weights and spatial weights, respectively, the channel and spatial attention modules enhance the model's sensitivity to certain channel attributes and spatial locations, hence altering the feature representation of those channels and spatial locations. The attention module can adaptively change the weights of channel and spatial features for improved feature extraction and fusion. Once the initial attributes are reformed, the IY3TM detects and classifies the turmeric plant diseases. The test outcomes reveal that the ConViT-TDD model accomplishes an overall accuracy of 93.16% on the collected turmeric plant diseases images which is contrasted with the classical CNN models. 2025 2025 61 74 10.54216/JISIoT.160106 https://www.americaspg.com/articleinfo/18/show/3581