• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2023, Volume: 16, Issue: Special Issue 2, Pages: 1-5

Original Article

Smart Agriculture via Object Detection

Received Date:23 March 2023, Accepted Date:26 June 2023, Published Date:20 October 2023


Objective: To employ a Convolutional Neural Network (CNN) for plant species classification based on image data. Method: A dataset of 10,000 plant images was utilized, and the dataset was split into training, validation, and testing sets. The CNN model was trained on the training set and evaluated on the validation and testing sets. Class-wise accuracy and a confusion matrix were analyzed to assess the model's performance. Findings: The CNN model achieved an accuracy of 93%, outperforming traditional machine-learning approaches. High accuracies (>90%) were obtained for 40 out of 50 plant species. However, certain species showed lower accuracies, indicating the need for further investigation and improvement. Novelty: This study contributes to the field of plant species classification by demonstrating the effectiveness of CNNs in achieving high accuracy. The results highlight the potential of automated plant species identification systems and emphasize the importance of exploring advanced techniques, such as transfer learning and ensemble methods, to enhance the model's performance.

Keywords: Convolutional Neural network (CNN), Deep Learning, Confusion matrix, Transfer learning, Plant species classification


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© 2023 Bharadwaj et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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