Aim:

            To address the critical issue of detecting tomato leaf diseases in agriculture by leveraging advanced techniques in Deep Learning and Computer Vision

Synopsis:

          The present work focuses on leveraging the TensorFlow API for the development of an advanced system for the detection of tomato leaf diseases. With the increasing importance of early disease detection in agriculture, the study integrates state of the art techniques in deep learning and computer vision to enhance the accuracy and efficiency of the disease identification. The primary objective is to harness the capabilities of the TensorFlow API, a powerful and widely used deep learning framework for the purpose of tomato leaf disease detection. This choice ensures access to a robust set of tools and resources for the model development. This object detection approach allow for the identification and localization of diseased regions on tomato leaves. By using TensorFlow models, the study aims to achieve an efficient and effective detection system. TensorFlow capabilities in optimizing model performance contribute to the system responsiveness and accuracy. The research addresses the scalability and generalization aspects of the model ensuring that the developed system is capable of adapting to different datasets and varying conditions. This enhances the applicability of the solution across diverse agriculture setting. The TensorFlow API is integrated with object detection techniques, emphasizing the importance of accurately identifying and delineating diseased regions on tomato leaves. This study includes a comprehensive validation process, assessing the performance of the TensorFlow API based model. By combining the power of deep learning with efficient object detection techniques. The proposed study seeks to reliable and scalable solution for early disease identification in tomato crops

Proposed System:

            The proposed method involves TensorFlow API-based object detection using the SSD MobileNetV2 architecture. First, prepare the dataset by collecting and annotating images with bounding boxes around the target objects, in this case the object relevant to your customized dataset. Next, convert the annotated dataset into the TensorFlow record format. Define the object classes and configure the training pipeline, specifying the SSD MobileNet architecture as the base model. Pre-trained weights can be used to expedite training, or you can train the model from scratch. Train the model using the configured pipeline, monitoring performance metrics such as loss and accuracy. Fine tune the model as needed based on evaluation results. Once the training is complete, export the training model to the TensorFlow saved model format. Finally, use the exported model for interference on new images to detect objects within the specified classes.