Determination of quality grade of tobacco leaf by image processing on correlated color temperature

Abstract

The most important factor in tobacco is quality. It is the sum of physical, chemical, and smoking properties that characterize quality. Post-harvest curing of leaves plays an important role in quality. In this study, an approach based on correlated color temperature (CCT) is proposed to determine the ideal curing time of tobacco leaves. While photographing leaf samples from different days during the curing of tobacco leaves, color correction was made using X-rite color checker to eliminate the negative effect of ambient light. CCT values were calculated from the repaired photographs using the McCammy method. The inclusion rate of pixels during the calculation is 97%. The equation of CCT variation depending on the day is fitted as CCT = (−0.1889 × day 3 ) + (4.8347 × day 2 ) − (115.59 × day) + 4919.2. RMSE (26.77 K) and high R 2 value were calculated as 98.8% in the equation. CCT values have been converted to quality grade (QG) values to be more understandable. As a result of the study, CCT values ranged between 5000 and 2500 K and corresponding QG values ranged between 79% and 105%. The ideal curing time was calculated as the 17th day after harvesting, the QG value for that day was 100% and the average CCT value was 3000 K.

A Hybrid Mobile Application for Quality Grade of Tobacco (Nicotiana tabacum L.) Using Correlated Color Temperature

“…To determine the most appropriate curing time, researchers investigated how the color of the leaves changed during curing in this study. They developed a mobile app that collects data on tobacco leaves and harvesting time, which can be used by farmers and tobacco experts (Odabas et al, 2022). The study found that the CCT values ranged from 5000 K to 2500 K, and the calculated QG values were between 79% and 105%.…”

SM-CycleGAN: crop image data enhancement method based on self-attention mechanism CycleGAN

“…Arun Pandian et al 22 developed a dataset of augmented plant leaf diseases using conventional image processing, such as image flipping, cropping, rotation, colour transformation, principal components analysis (PCA) colour enhancement and noise injection techniques. While photographing leaf samples at various times during tobacco roasting, Odabas, Mehmet Serhat 23 used the X-rite colour checker for colour correction to eliminate the negative effect of ambient light. However, all the above conventional methods for image data enhancement require manual extraction of lesion features or colour features and are limited in their effectiveness in processing similar disease features or colour features.…”

Valuation of the Quality Level of Bulgarian Tobacco from the Market Sector of “Basmi” Variety Group

“…Changes in any of these factors can result in significant differences in the chemical composition and, accordingly, in the smoking properties of tobacco. Therefore, plenty of research activity has been devoted to the analysis of these factors and their influence on tobacco quality, in order to steer them in the desired direction [4][5][6][7][8][9]. The inefficient resource use poses serious risks to the high-quality Oriental tobaccos.…”

“…The quality concept for Oriental tobaccos includes technological characteristics (sensory, physical, chemical) and different aspects of market demand, varying according to the type of product to be produced and consumer preferences [5,12]. As stated in a recent study [13], "Its [tobacco] grading is the key action that governs the direction of the product in all stages from processing, marketing and finally to the end-product" [13].…”