Tomato diseases cause large output losses and represent a serious danger to the world's economy and productivity. Manual evaluation is expensive and difficult. Modern farming methods can be replaced with more effective ones by utilizing artificial intelligence and cutting-edge technology. The objective of this research is to create a Deep Learning (DL) technique that can precisely identify tomato diseases from images of the leaves in an Internet of Things (IoT) environment. Using the PlantVillage Dataset as a benchmark, the proposed MLP-Mixer model was assessed with other well-known transfer learning methods, including VGG16, AlexNet, VGG19, and FNet. A comparison of each model's parameter complexity and performance assessments was part of the analysis. With an area under the characteristic curve of 99.69% and a peak accuracy of 98.34%, the MLP-Mixer method produced impressive results.