Fruit Plant Recognition and Classification from Plant Leaves using Deep Learning, CNN Models
Ajahar Ismailkha Pathan1, Swati Pandey2
1Ajahar Ismailkha Pathan, Department of Computer Science and Engineering, Oriental University, Indore (M.P.), India.
2Dr. Swati Pandey, Department of Computer Science and Engineering, Oriental University, Indore (M.P.), India.
Manuscript received on 26 September 2025 | First Revised Manuscript received on 10 October 2025 | Second Revised Manuscript received on 21 October 2025 | Manuscript Accepted on 15 November 2025 | Manuscript published on 30 November 2025 | PP: 16-25 | Volume-14 Issue-4, November 2025 | Retrieval Number: 100.1/ijrte.D830314041125 | DOI: 10.35940/ijrte.D8303.14041125
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Plants are an integral part of human life, and the ability to identify a fruit plant from its leaf image is both fascinating and challenging. Advances in image processing and pattern recognition have made it feasible to perform plant identification using digital images. Machine learning (ML) and convolutional neural network (CNN) models have demonstrated strong capabilities in handling texture-related features in image processing tasks, including segmentation. In this Paper, we present an approach that utilises ML and CNN models, including AlexNet, Inception, ResNet, LeNet, VGG Net, MobileNet, DenseNet, and GoogLeNet. These models are employed for classifying fruit plants through leaf images, achieving promising performance on leaf image datasets. Among the evaluated CNN models, MobileNet achieved the highest performance with 94.81% training, 99.57% validation, and 99.44% test accuracy, outperforming all others. LeNet, AlexNet, and ResNet also showed strong results above 93%, while DenseNet, GoogLeNet, and VGGNet achieved moderate accuracy. Inception performed the weakest, confirming MobileNet as the most efficient and reliable model for fruit plant leaf classification.
Keywords: Fruit Recognition, Fruit Classification, Feature Extraction, and Texture Extraction.
Scope of the Article: Computer Vision