Plant Health Detection System using Deep-Learning

Er. Ankit; Rahul Sharma; Rahul Yadav; Vuribindi Sai Charan Reddy Reddy; Rakesh Kumar Kumar; Vishal Chaudhary Chaudhary; Anil Kumar Chaudhary

doi:10.32628/CSEIT2410224

Authors

Er. Ankit Assistant Professor, Department of CSE, Lovely Professional University, Phagwara, Punjab, India Author
Rahul Sharma B.Tech Scholar, Department of CSE, Lovely Professional University, Phagwara, Punjab, India Author
Rahul Yadav B.Tech Scholar, Department of CSE, Lovely Professional University, Phagwara, Punjab, India Author
Vuribindi Sai Charan Reddy B.Tech Scholar, Department of CSE, Lovely Professional University, Phagwara, Punjab, India Author
Rakesh Kumar B.Tech Scholar, Department of CSE, Lovely Professional University, Phagwara, Punjab, India Author
Vishal Chaudhary B.Tech Scholar, Department of CSE, Lovely Professional University, Phagwara, Punjab, India Author
Anil Kumar B.Tech Scholar, Department of CSE, Lovely Professional University, Phagwara, Punjab, India Author

DOI:

https://doi.org/10.32628/CSEIT2410224

Keywords:

Plant Heath Detection System, CNN, Machine Learning, Computer Vision

Abstract

Food security, environmental stability, and agricultural output are all significantly impacted by plant health. Expert visual inspection is a common component of traditional plant health assessment techniques, although it can be laborious, subjective, and prone to human mistake. Using advances in computer vision and machine learning, there has been an increasing interest in applying deep learning techniques for automated plant health diagnosis in recent years. This study provides a thorough analysis of deep learning- based plant health detection systems, covering a wide range of topics including model architectures, training methodologies, dataset collecting and preprocessing, and performance evaluation measures. The field's main obstacles and prospects are noted, such as the lack of datasets, the inability of the model to generalize to many plant species and environmental circumstances, and the inability of the model to scale to large-scale agricultural settings.

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References

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