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Kerala Agricultural University, Thrissur

The history of agricultural education in Kerala can be traced back to the year 1896 when a scheme was evolved in the erstwhile Travancore State to train a few young men in scientific agriculture at the Demonstration Farm, Karamana, Thiruvananthapuram, presently, the Cropping Systems Research Centre under Kerala Agricultural University. Agriculture was introduced as an optional subject in the middle school classes in the State in 1922 when an Agricultural Middle School was started at Aluva, Ernakulam District. The popularity and usefulness of this school led to the starting of similar institutions at Kottarakkara and Konni in 1928 and 1931 respectively. Agriculture was later introduced as an optional subject for Intermediate Course in 1953. In 1955, the erstwhile Government of Travancore-Cochin started the Agricultural College and Research Institute at Vellayani, Thiruvananthapuram and the College of Veterinary and Animal Sciences at Mannuthy, Thrissur for imparting higher education in agricultural and veterinary sciences, respectively. These institutions were brought under the direct administrative control of the Department of Agriculture and the Department of Animal Husbandry, respectively. With the formation of Kerala State in 1956, these two colleges were affiliated to the University of Kerala. The post-graduate programmes leading to M.Sc. (Ag), M.V.Sc. and Ph.D. degrees were started in 1961, 1962 and 1965 respectively. On the recommendation of the Second National Education Commission (1964-66) headed by Dr. D.S. Kothari, the then Chairman of the University Grants Commission, one Agricultural University in each State was established. The State Agricultural Universities (SAUs) were established in India as an integral part of the National Agricultural Research System to give the much needed impetus to Agriculture Education and Research in the Country. As a result the Kerala Agricultural University (KAU) was established on 24th February 1971 by virtue of the Act 33 of 1971 and started functioning on 1st February 1972. The Kerala Agricultural University is the 15th in the series of the SAUs. In accordance with the provisions of KAU Act of 1971, the Agricultural College and Research Institute at Vellayani, and the College of Veterinary and Animal Sciences, Mannuthy, were brought under the Kerala Agricultural University. In addition, twenty one agricultural and animal husbandry research stations were also transferred to the KAU for taking up research and extension programmes on various crops, animals, birds, etc. During 2011, Kerala Agricultural University was trifurcated into Kerala Veterinary and Animal Sciences University (KVASU), Kerala University of Fisheries and Ocean Studies (KUFOS) and Kerala Agricultural University (KAU). Now the University has seven colleges (four Agriculture, one Agricultural Engineering, one Forestry, one Co-operation Banking & Management), six RARSs, seven KVKs, 15 Research Stations and 16 Research and Extension Units under the faculties of Agriculture, Agricultural Engineering and Forestry. In addition, one Academy on Climate Change Adaptation and one Institute of Agricultural Technology offering M.Sc. (Integrated) Climate Change Adaptation and Diploma in Agricultural Sciences respectively are also functioning in Kerala Agricultural University.

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Subject: Agricultural Statistics and Informatics × Author: KAU × End date: 2022 × Start date: 2022 × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    Statistical assessment of banana ripening using smartphone - based images
    (Department of Agricultural Statistics, College of Agriculture, Vellayani, 2022) Haritha, R Nair; KAU; Pratheesh, P Gopinath
    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.