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BIOCHEMISTRY OF FASTING – A REVIEW ON METABOLIC SWITCH AND AUTOPHAGY.
Volume - 13 | Issue-1
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ONE-POT ENVIRONMENT FRIENDLY SYNTHESIS OF IMINE DERIVATIVE OF FURFURAL AND ASSESSMENT OF ITS ANTIOXIDANT AND ANTIBACTERIAL POTENTIAL
Volume - 13 | Issue-1
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MODELING AND ANALYSIS OF MEDIA INFLUENCE OF INFORMATION DIFFUSION ON THE SPREAD OF CORONA VIRUS PANDEMIC DISEASE (COVID-19)
Volume - 13 | Issue-1
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INCIDENCE OF HISTOPATHOLOGICAL FINDINGS IN APPENDECTOMY SPECIMENS IN A TERTIARY CARE HOSPITAL IN TWO-YEAR TIME
Volume - 13 | Issue-1
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SEVERITY OF URINARY TRACT INFECTION SYMPTOMS AND THE ANTIBIOTIC RESISTANCE IN A TERTIARY CARE CENTRE IN PAKISTAN
Volume - 13 | Issue-1
ESTIMATE ACCURACY IN IMAGE PLANT DISEASES DETECTION USING CONVOLUTIONAL NEURAL NETWORK COMPARED WITH FULLY CONNECTED NEURAL NETWORK
Main Article Content
Abstract
Aim: To estimate accuracy in Image plant disease detection using Convolutional Neural Network over Fully Connected Neural Network. Materials and Methods: Convolutional Neural Network and Fully Connected Neural Network are implemented in this research work. Sample size is calculated using G power software and determined as 10 per group with pretest power 80%. Results and Discussion: Convolutional Neural Network provides a higher of 89.00 compared to Fully Connected Neural Network with 81.52 in predicting plant disease in plant diseases detection. There are statistically significant differences between study groups with p = 0.035 (p<0.05). Independent T-test value states that the results in the study are insignificant. Conclusion: Convolutional Neural Network gives better accuracy then Fully Connected Neural Network.
