Nitrogen is an essential nutrient element required for optimum crop growth and yield. If a specific amount of nitrogen is not applied to crops, their yield is affected. Estimation of nitrogen level in crops is momentous to decide the nitrogen fertilization in crops. The amount of nitrogen in crops is measured through different techniques, including visual inspection of leaf color and texture and by laboratory analysis of plant leaves. Laboratory analysis-based techniques are more accurate than visual inspection, but they are costly, time-consuming, and require skilled laboratorian and precise equipment. Therefore, computer-based systems are required to estimate the amount of nitrogen in field crops. In this paper, a computer vision-based solution is introduced to solve this problem as well as to help farmers by providing an easier, cheaper, and faster approach for measuring nitrogen deficiency in crops. The system takes an image of the crop leaf as input and estimates the amount of nitrogen in it. The image is captured by placing the leaf on a specially designed slate that contains the reference green and yellow colors for that crop. The proposed algorithm automatically extracts the leaf from the image and computes its color similarity with the reference colors. In particular, we define a green color value (GCV) index from this analysis, which serves as a nitrogen indicator. We also present an evaluation of different color distance models to find a model able to accurately capture the color differences. The performance of the proposed system is evaluated on a Spinacia oleracea dataset. The results of the proposed system and laboratory analysis are highly correlated, which shows the effectiveness of the proposed system.

Multi-focus image fusion is the process of combining focused regions of two or more images to obtain a single all-in-focus image. It is an important research area because a fused image is of high quality and contains more details than the source images. This makes it useful for numerous applications in image enhancement, remote sensing, object recognition, medical imaging, etc. This paper presents a novel multi-focus image fusion algorithm that proposes to group the local connected pixels with similar colors and patterns, usually referred to as superpixels, and use them to separate the focused and de-focused regions of an image. We note that these superpixels are more expressive than individual pixels, and they carry more distinctive statistical properties when compared with other superpixels. The statistical properties of superpixels are analyzed to categorize the pixels as focused or de-focused and to estimate a focus map. A spatial consistency constraint is ensured on the initial focus map to obtain a refined map, which is used in the fusion rule to obtain a single all-in-focus image. Qualitative and quantitative evaluations are performed to assess the performance of the proposed method on a benchmark multi-focus image fusion dataset. The results show that our method produces better quality fused images than existing image fusion techniques.