A Comprehensive Machine Learning Approach to Analyzing Lemongrass (Cymbopogon citratus) Leaf Dataset for Agricultural Innovation
Ved Thawri
Vishwakarma University, Pune, India
Abstrct:
This paper describes an innovative convolutional neural network (CNN) model for classifying lemongrass pictures into three categories: good, unhealthy, and dried. The model design comprises numerous critical components, including convolution, pooling, dropout, and dense layers, which collectively lead to its excellent accuracy in photo categorization tasks. We present a detailed overview of the dataset preparation, which includes collecting and labelling a large number of lemongrass photos. Furthermore, the model training and validation processes are extensively explained, ensuring that the CNN model is resilient and reliable. Our findings show that the CNN model is highly accurate in discriminating between the three categories of lemongrass health. Using this model, farmers and agricultural researchers can receive significant insights into the health of lemongrass crops, allowing for timely interventions and improved crop management. Our findings indicate that this CNN model could be an effective tool for enhancing precision agriculture, ultimately contributing to increased production.
Keywords:
Lemongrass, Image Classification, Convolutional Neural Network, Machine Learning, Agricultural Technology
Published on: 05-2024
Journal Name: Science Management Design Journal
Volume: 02
Issue: 01
Pages: 22-29
Month: May
Year: 2024