Estimation of Paddy leaves Chlorophyll Content Using Convolutional Neural Network (CNN)

Abstract

In contemporary agriculture, the demand for cutting-edge machine-learning techniques to elevate crop assessment and management is paramount. This study introduces an innovative approach employing a Convolutional Neural Network (CNN) with Inception_v3 as its base model to estimate chlorophyll levels in paddy leaves. The primary aim is to craft a robust, precise model capable of non-destructively predicting chlorophyll content, promising substantial improvements in the efficiency of evaluating paddy crop health and nutritional status. The dataset comprises 566 images of paddy leaves, spanning 122 unique chlorophyll content levels. A meticulous data partitioning strategy allocates 244 images for model training, with 122 and 180 images for validation and testing, respectively. Model performance metrics include a test loss of 1.19 and a test accuracy of 0.81. Leveraging the Inception_v3 architecture empowers the CNN model to extract intricate, distinguishing features from paddy leaf images. This capability enables the model to discern subtle variations in chlorophyll content across different classes, underpinning its promising predictive prowess. Future research directions may explore potential model enhancements and dataset expansion, marking significant progress toward revolutionizing crop health assessment in modern agriculture.