Deep Learning-Based Analysis of Neem Leaf and Tree Age for Optimal Utilization

Abstract

A custom dataset of Neem leaves and trees was collected and categorized into multiple age and health classes, forming the basis for developing an automated deep learningbased classification framework. Since Neem is a tree of high medicinal, ecological and cultural importance, accurate identification of its leaf and tree age can contribute to better utilization of its properties and support ecological conservation. A series of preprocessing steps were performed to ensure that the dataset was suitable for deep learning applications . These included preprocessing properly the images to a consistent resolution , normalizing pixel values to bring them within a standard range and applying extensive data augmentation techniques such as rotations , flips , scaling and color modifications . For the image classification task , transfer learning was used on several pretrained convolutional neural network (CNN) architectures , including DenseNet121 , ResNet50 , MobileNet , NASNet and EfficientNet . Transfer learning allowed the models to leverage previously learned feature representations from largescale image datasets like ImageNet while being fine-tuned to the specific characteristics of Neem leaf and tree images . By giving an automated and reliable method of Neem leaf and tree age classification , the framework reduces the dependency on manual observation , which is often subjective and error-prone , and instead provides a proper solution that can be supportive for medicinal research , ecological monitoring and resource optimization , ultimately contributing to both scientific advancement and environmental sustainability .