Tensor Flow-Based Capsicum Classification: An Intelligent Mobile Interface Agriculture Crop Monitoring

Utilizing state-of-the-art technologies is essential for effective crop management and monitoring in modern agriculture. The objective of this project is to create a sophisticated mobile interface specifically designed for the classification of bell peppers, or capsicum, in agricultural settings. The main aim of this project is to develop a strong image classification model that can recognize different characteristics of capsicum plants, such as their developmental stage and general health, by using TensorFlow, an open-source machine learning framework. This research uses a methodical approach that begins with gathering and preprocessing a large and varied dataset of labeled photos of capsicums. Subsequently, a convolutional neural network (CNN) is meticulously built and trained using TensorFlow, leveraging transfer learning techniques to enhance the model's functionality. The trained model is transformed into TensorFlow Lite format to enable smooth deployment on mobile platforms. Using a sophisticated image classification model trained on large datasets of tagged photos of capsicums, the main goal is to accurately classify plants according to their growth stages and health status. The project aims to maximize the model's capacity to identify subtle characteristics of capsicum plants by employing transfer learning strategies and pre-trained models. For the project to function smoothly, the trained TensorFlow model must be converted into TensorFlow Lite format. This allows the model to be integrated into an intelligent mobile interface. With the help of this transformation, users can take advantage of an intuitive and mobile-responsive interface to take pictures of capsicum plants in real time with their device's camera.