Author(s) :   Rohit Ninawe¹, Prerna Dangra²

Abstract : The automatic detection and classification of plant leaves is crucial for advancements in agricultural automation and botanical research. Traditional manual methods for identifying plant species based on leaf characteristics are labor-intensive and prone to error. Convolutional Neural Networks (CNNs) have demonstrated exceptional performance in various image classification tasks and offer a promising solution for this challenge. This paper presents a CNN-based approach for the detection and classification of plant leaves. The proposed model was trained and tested on a diverse dataset, achieving high accuracy and robustness under varying environmental conditions. The results indicate that CNNs can significantly enhance the efficiency and accuracy of leave detection systems, paving the way for more sophisticated applications in agriculture and plant sciences.

Keywords: agricultural, environment, plant.

DOI : 10.61161/ijarcsms.v12i7.34

Pages : 277-283

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