An examination of Groundnut Crop Leaves to Identify the Chlorophyll Deficiency Before Visible Symptoms.

Nitrogen nutrient concentration is predominant in crop field monitoring. Noticing the deficiency pattern, providing sufficient amounts of nutrients will help to recover the crop. Sometimes it may not be possible to reverse the crop in cases of deep deficiency. It will affect the quality and quantity of the crop product in yield time. Hence, timely identification is important. Therefore, detection of chlorophyll deficiency in groundnut leaves (DCDGL) system is proposed here for nutrient deficit recognition, before a notable pattern emerges. In first phase, groundnut leaf images are collected and clustered based on chlorophyll measurement. Then, different image processing techniques like image compression, resizing, and noise reduction filters are applied and analyzed on leaf images to enhance the images and secure the predominant features. The median filter is fixed because it provides high quality output with a low noise percentage. The quality of an image is measured in decibels with the help of the peak signal to noise ratio (PSNR). Then, in the next phase, colour features and grey level co-occurrence matrix texture features are extracted and given to developed intensified multinomial classification (IMC) model to detect nutrient deficiencies in groundnut leaves. The obtained average accuracy of IMC model is 97%. The DCDGL system's performance is tested with state-of-the-art techniques like K-nearest neighbour and support vector machine. The developed model performance is superior to state-of-the-art techniques regarding accuracy. The appropriate image quality analysis and pre-processing approach helped achieve less processing time to predict nutrient deficiency before tangible symptoms. [ABSTRACT FROM AUTHOR]