TY  - BOOK
AU  - Haritha R Nair
AU  - Pratheesh P Gopinath(G)
TI  - Statistical assessment of banana ripening using smartphone - based images
U1  - 630.31 
PY  - 2022///
CY  - Vellayani
PB  - Department of Agricultural Statistics, College of Agriculture
KW  - Agricultural Statistics
KW  - Banana
N1  - M Sc
N2  - The research work entitled “Statistical assessment of banana ripening using 
smartphone-based images” was carried out at College of Agriculture, Vellayani during 
the period 2019 to 2021. The objectives were the development of suitable model to 
establish the relationship between Total Soluble Solids (TSS) and L*(lightness), 
a*(green-red ratios), b*(blue-yellow ratios) values and for prediction of TSS values 
using L*, a*, b* values. Development of a protocol for accurate data collection to assess 
TSS content in Banana using smart-phone-based images. Good quality Nendran variety 
with only minor shape and peel colour flaws were obtained from a nearest field 
randomly chosen for the study. Each time 3 hands at the ripening stage 1 (green) with 
10 fingers by hand were collected. The fruits were stored in a normal day/ night cycle. 
Bananas were taken randomly from each hand and their color changes and development 
of brown spots were measured daily during 10-12 days. Banana samples were placed 
on the table covered with a non-reflecting white paper as a background of the image. 
For white light illumination, two of 36 W fluorescent lamps were fixed at ceiling above 
the experiment setup. Three smartphones were used for image acquisition. Smart 
phones were placed at a distance of 20 cm above the banana. Samples of banana were 
blended using a fruit juicer. The TSS were determined using a digital refractometer. For 
the images obtained, RGB and L*a*b* were extracted using ImageJ software. The 
observations on TSS, R, G, B, L*, a*, b* were used for fitting regression models after 
splitting the data into train (80%) and test (20%) sets. 
 When linear model was fitted between TSS and R, G, B values for all the three 
devices, each of the independent variables were found to be significant. Adjusted Rsquared values obtained were 0.80, 0.80, and 0.84 for the three devices. It means about 
80% of the variation in the TSS was explained by R, G, B values. For the predicted 
values of TSS R-squared values were 0.84, 0.90, and 0.95. Hence linear model was 
found to be better fit for predicting TSS. Since RGB color model is device dependent 
model, it may not always represent the same colour on different devices. But in case of 
CIE L*a*b*, it is device independent and shadows and areas of glossiness on the object 
surface had less impact. Therefore, linear model was fitted between TSS and L*, a*, b* 
values. Adjusted R-squared values obtained were 0.78, 0.81, and 0.85 for the three 
126 
devices. For the predicted TSS values R-squared values were 0.84, 0.76, and 0.95. 
Therefore, linear model between TSS and RGB model found to predict TSS much 
accurately than L*a*b* color space when prediction accuracy was compared. 
 On visualization of data, TSS and L*a*b* found to have non-linear relationship 
for all the devices. When spline regression was fitted between TSS and L*, a*, b* values 
R-Squared obtained were 0.91, 0.90, and 0.89, which was higher compared to Rsquared values for linear model. Also, deviance explained by the models were 92%, 
92.3%, and 90.7% for corresponding device 1,2 and 3. Therefore, spline regression 
found to be better model for TSS and L*, a*, b* data and for prediction of TSS values. 
 Protocol for accurate data collection was developed with modification in the 
procedure performed. Possibility of Deep learning was explored in the study using 
CNN. Convolutional neural network (CNN) was developed using 3 categories Raw 
(TSS 4-10), Medium (TSS 11-17) and Ripe (TSS 18-32) with 30 samples each. 25 
images from each category were taken as training set and 5 were taken as test set. 100 
epochs were performed to mitigate overfitting and to increase the generalization 
capacity of the neural network. Model evaluation of training set gave an accuracy of 
84% with loss value 0.45. For the training set, all 25 from ripe category were able to 
identify into that particular category. In case of raw 24 were identified as raw with 1 
identified as medium. For medium 14 were identified as medium,3 identified as ripe 
and 8 identified as raw. Model evaluation of test set provided 73% accuracy with 0.81 
loss. The model successfully classified 5 ripe bananas, 4 raw bananas (1 classified as 
medium) and 2 medium bananas (3 classified as raw). 
 The results of the research work to identify the best fitting model concluded that 
RGB model found to predict TSS much accurately than L*a*b* color space when linear 
regression model was fitted and spline regression model was found to be the best fit for 
L*, a*, b* and TSS values, R-squared values were much higher with a good percentage 
of variation explained. The CNN developed classified images into raw, medium, and 
ripe with approximate accuracy of 74%. Therefore, CNN can be used to predict range 
of TSS in no time, if a large number of images are uploaded into this model. The CNN 
can be optimized further with higher number (atleast 10,000 samples) of samples to 
improve the efficiency of classification
UR  - https://krishikosh.egranth.ac.in/handle/1/5810194728
ER  -