• Srikanth S P
• HemanthKumar B R
• Dr. Lata B T
• Dr. Venugopal K R

We propose a novel approach that utilize machine learning algorithm for plant growth monitoring that enables real-time analysis and optimization, fostering sustainable agriculture practices. Machine learning algorithms can analyze data with high precision, detecting subtle changes in plant growth that might be missed by human observation or basic sensors. This leads to more accurate monitoring and better-informed decisions. Machine learning models can continuously learn and improve over time as they are fed with more data. Convolutional Neural Networks (CNN) are employed for image analysis. Plant dimension such as height and width measurements are captured. This leads to increasingly accurate predictions and insights into plant growth dynamics. Overall, the integration of machine learning technology into plant growth monitoring offers significant improvements in efficiency, accuracy, and decision-making, ultimately leading to better plant health and increased agricultural productivity.

Automated monitoring, Crop monitoring, Leaf area index, Precision agriculture, Smart agriculture

"PGM: Plant Growth Monitoring using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.f534-f541, May-2024, Available :http://www.jetir.org/papers/JETIR2405561.pdf

"PGM: Plant Growth Monitoring using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppf534-f541, May-2024, Available at : http://www.jetir.org/papers/JETIR2405561.pdf