Abstract:
This paper convolutional neural networks (CNNs) have shown great promise for the
categorization and chemical feature extraction of lemon leaves, among other
agricultural applications. This paper investigates the use of CNNs, namely MobileNet,
which achieves a maximum accuracy of 84%, for the precise age classification and
chemical composition prediction of lemon leaves. In order to discern between the
oldest, middle-aged, and young age groups and to forecast nutrient levels like
potassium, calcium, magnesium, phosphate, and sulfate, the research uses deep learning
algorithms to analyze leaf photos and extract complex information.
Preprocessing leaf pictures, training MobileNet on an extensive dataset, and assessing
model performance using measures like as accuracy, precision, recall, and F1-score are
all part of the technique. The outcomes show how well MobileNet works to achieve
high accuracy in tasks involving both chemical feature prediction and classification.
By giving farmers strong tools to track leaf health, improve nutrient management plans,
and increase crop output in a sustainable way, this work advances precision agriculture.
To further improve the scalability and usefulness of CNN-based techniques in
agricultural contexts, future research areas include expanding the dataset, investigating
ensemble learning strategies, and incorporating real-time applications.
