Cinnamon Maturity Identification Using CNN

Abstract

"Accurately determining the maturity of cinnamon bark is crucial for maximizing product quality and economic value. Traditional methods of assessing bark maturity rely on manual inspection, which is subjective, labor-intensive, and time-consuming. This study explores the development of a mobile application powered by Convolutional Neural Networks (CNNs) to automate cinnamon bark maturity identification. The mobile app allows users, including farmers and agricultural professionals, to easily assess bark maturity using smartphone cameras, making the solution accessible and practical.

A dataset of high-resolution cinnamon bark images was collected, capturing various stages of maturity. These images were preprocessed and used to train and validate CNN models such as ResNet, MobileNet, and EfficientNet. The models were fine-tuned to identify subtle differences in texture, color, and patterns that indicate maturity. Integration of the trained CNN model into the mobile app was achieved using lightweight frameworks optimized for mobile devices, ensuring fast and accurate predictions with minimal computational resources.

The mobile application provides a user-friendly interface, allowing real-time image capture, analysis, and classification of cinnamon bark maturity. Testing results demonstrate high accuracy and reliability, offering a scalable and efficient solution for maturity assessment. This approach significantly reduces reliance on manual methods, enhances consistency, and empowers farmers with modern technology. Future enhancements will focus on improving app performance in varying environmental conditions, expanding the image dataset, and integrating additional features like yield prediction and harvest scheduling."