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The photochemical behavior of xanthene dyes (fluorescein, erythrosine, and eosin) with colloidal SnO2nanoparticles was probed by UV-visible, steady state, and time resolved fluorescence measurements. The prepared SnO2nanoparticles were characterized by using UV-visible and powder XRD measurements. The xanthene dyes were adsorbed on the surface of colloidal SnO2nanoparticles through electrostatic interaction. Apparent association constant (Kapp) was calculated from the relevant fluorescence data. The larger value of apparent association constant indicates a strong association between xanthene dyes and SnO2nanoparticles. The fluorescence quenching is mainly attributed to electron transfer from the excited state xanthenes to the conduction band of colloidal SnO2. The electron transfer mechanism was explained based on the Rehm-Weller equation as well as the energy level diagram.

The major objective of the present study was to explore if Artificial Neural Network (ANN) models with back propagation could efficiently predict the rice yield under various climatic conditions; ground-specific rainfall, ground-specific weather variables and historic yield data. The back propagation algorithm will calculate each expected weight using the error rate as the activity level of a unit was altered.  The errors in the model during the training phase were solved during the back-propagation. The paddy yield prediction took various parameters like rainfall, soil moisture, solar radiation, expected carbon, fertilizers, pesticides, and the long-time paddy yield recorded using Artificial Neural Networks. The R2 value on the test set was found to be 93% and it showed that the model was able to predict the paddy yield better for the given data set. The ANN model was tested with learning rates of 0.25 and 0.5. The number of hidden layers in the first layer was 50 and in the second hidden layer was 30. From this, the testing value of R square was 0.97. The observations with the ANN Model showed that i) the best result for the test set was  R2 value of 0.98, ii) the two hidden layers kept with 50 neurons in the first layer and 30 neurons in the second one, iii) the learning rate was of 0.25. With all these configurations, maximum yield is possible from the paddy crop.

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