Analysis of leaf nutrient concentration in different crop species using image processing techniques

Colour image analysis plays a vital role in agriculture by reducing human intervention in field monitoring and substituting the visual decision-making of farmers. Several image processing methods and leaf colour charts (LCC) have been developed by researchers, especially for predicting nitrogen nutrient concentration in plants. But there is no generalized technique to find the nutrient status of different plants. To accomplish this research gap, an examination is going on with parts of plant images by researchers. Therefore, the prediction of chlorophyll association with colour indices (PCACI) method is proposed to determine the relationship between chlorophyll content and colour indices. The average chlorophyll value (ACV) of five different crop leaves is initially measured. Simultaneously, the same leaves are captured to find the relationship with chlorophyll content. Because chlorophyll has a linear relationship with leaf nitrogen content. Two colour indices were introduced, namely intense greenness percentage (IGP) and intense green colour index (IGCI), which are extracted from leaf images in hexadecimal decimal (HD) format. The HD values are obtained by applying the k-means clustering algorithm to the captured leaf image dataset. Hexadecimal-based colour names are analyzed with leaf images and suggested to make a leaf colour chart for the mentioned crop leaves. Based on the evaluation of IGP and IGCI with ACV with different crop leaf images, the obtained results vary from crop to crop. Analyzing the correlation coefficient of ACV with IGP reveals a significant relationship with curry leaves, sapodilla, and tulsi leaves. But the linear relationship is acceptable only with curry leaves. While analyzing ACV with IGCI, it shows considerable association and a linear relationship with curry leaves and sapodilla. The result implies that the derived colour indices can be used for predicting the nitrogen content of some other crops after the proper analysis.