TinyML is an emerging field of machine learning that focuses on developing and deploying models on low-power microcontrollers in embedded systems and Internet of Things devices. Edge Impulse and TensorFlow are two popular machine learning platforms that can be used for TinyML model development and implementation for various applications, such as smart crop disease detection. Farmers can improve crop yield and quality through the employment of real-time monitoring facilitated by TinyML models. These models are operational on microcontrollers integrated with cameras or sensors, enabling the timely identification of crop diseases and pest infestations. In this paper, we compare the workflow of both platforms in terms of data collection and preprocessing, model development and training, model evaluation and testing, and model deployment and inference. We use a dataset of 12,344 raw images and 7454 augmented images of healthy and diseased maize leaves to evaluate each platform’s features, performance, and usability for detecting crop diseases. We also discuss the advantages and disadvantages of each platform in terms of ease of use, flexibility, scalability, reliability, and integration with hardware and cloud services. Our results demonstrate that Edge Impulse has a slight edge over TensorFlow in terms of ease of use, reliability, and memory footprint, while TensorFlow offers more flexibility, customization options and higher accuracy. In conclusion, while both platforms are suitable for crop disease detection, combining them allows for leveraging the powerful training capabilities of TensorFlow for complex neural networks, along with the simplicity of Edge Impulse for deploying them on low-powered edge devices.