Rice plant nitrogen level assessment through image processing using artificial neural network

Abstract

This paper presents a program which identifies the 4-panel LCC equivalent of rice plants using image processing techniques and pattern recognition of the Backpropagation neural network. Images of the fully expanded healthy leaves were captured by digital camera and processed through RGB acquisition, color transformation, image enhancement, image segmentation and feature extraction procedures. The extracted features were computed using basic statistical methods, then served as the input to the neural network for LCC panel identification. Thirty (30) samples of IRR 82372H -Mestiso 26 variety were tested; divided into three sets with 10 leaf samples per field. The system was observed to provide an accuracy of 93.33%.

Nitrogen Deficiency Level Assessment Device for Rice (Oryza sativa L.) and Maize (Zea mays L.) using Classification Algorithm-based Spectrophotometry

“…For many years, researchers explored the use of image processing to assess and detect plant N-levels. [10]- [12] On top of image processing, supervised classification algorithms such as artificial neural network [13] and support vector machine [14] have also been used to eliminate the subjectivity in the analysis of the N-level in rice plant [15] and estimating leaf chlorophyll content in maize [16] as in the case of using an LCC. In some cases where image processing is used to retrieve leaf chlorophyll content using smartphones [17], additional hardware is usually utilized to neutralize the effect of ambient light and to prevent the noise from affecting the image.…”