Improved Plant Phenotyping System Employing Machine Learning Based Image Analysis - Koushik Reddy Chaganti, C. Sasikala, G.Sirisha, Shravani Amar, Ravindra Changala, Koppuravuri Gurnadha Gupta, Chinnala Balakrishna (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The objective of this paper was to propose an advanced platform for phenotyping plants using machine learning and image analysis. Phenotyping is the process of analyzing and measuring the physiological characteristics of a part or whole plant like the shape of a leaf, the color of the flower, or the structure of the root. It helps to understand the genetic and environmental factors that influence plant growth and productivity. It is used to provide new plant breeding programs. The images will be pre-processed to standardize their size and format, and relevant features will be extracted for use in training a machine learning model. The model will be trained to classify the images based on their phenotypic traits and will be validated for accuracy. The trained model will then be integrated into the phenotyping platform for automatic analysis and classification of new images. The result will be a tool that can aid in the study of plant phenotypes – crop yield prediction, type classification, crop growth, etc.