COMPARATIVE ANALYSIS OF CUSTOMIZED CNN AND TEACHABLE MACHINE IN PLANT LEAVES IMAGES - Dave, Parth R.; Pansiniya, Viral B.; Patel, Bhaveshkumar P.; Trivedi, Bhoomi H.; Patel, Yogesh B. (ShodhKosh: Journal of Visual and Performing Arts, 2024)
Accurate identification of plant species is critical for biodiversity monitoring, ecological studies, and agricultural management. Traditional manual identification methods are time-consuming and prone to human error, necessitating automated solutions. This research presents a comparative analysis between a customized Convolutional Neural Network (CNN) model and Google's Teachable Machine for plant species classification using the Leaves Image Dataset. The customized CNN is designed with multiple convolutional layers and optimized hyper parameters, while the Teachable Machine offers a no-code AI solution. Experimental results demonstrate that the customized CNN outperforms the Teachable Machine in terms of accuracy, precision, recall, and F1-score. The study highlights the strengths and limitations of both approaches, providing insights for deploying species classification models in real-world scenarios.