Abstract

Fungi have been identified as a major threat to crop production in the world. In this study, methods of improving the performance of plant disease detection and prediction using artificial neural network techniques are presented. The hyperspectral fungi dataset of 21 plant species were collected and trained using backpropagation algorithms of an artificial neural network to improve the conventional hyperspectral sensor. The system was modelled using self-defining equations and universal modelling diagrams and then implemented in the neural network toolbox in Matlab. The system was tested validated and the result showed a fungi detection accuracy of 96.61% and the percentage increment was 19.53%.

Keywords

  • plant disease
  • artificial neural network
  • backpropagation algorithms

References

  1. Tanja Folnovic (2019) “Farm Revolution -Sensors for Crop Pest Detection” article on Current and Prospective Methods for Plant Disease Detect
  2. Katrin Anne M. (2016) “Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping” Published Online: 18 Jan 2 016 Pages: 241-251;Vol. 100, No. 2.
  3. Sen. Heineken Lokpobiri; (2020) “Minister of state for agriculture and rural development, Nigerian; Nigerian food inflation, trending economy.
  4. Sharma P, Hans and Gupta S (2020) ‘classification of plant leaf disease using machine and image processing techniques’ 10th International Conference on cloud computing, data science and engineering, India; pp 480-484.
  5. Hetrick, R.L., Rodrigo, O.D. and Bocchini, C.E., 2020. Addressing Pandemic-Intensified Food Insecurity. Pediatrics, 146(4).
  6. Pranjali B. Padol, Prof. AnjilA.Yadav, (2016) "SVM Classifier Based Grape Leaf Disease Detection" Conference on Advances in Signal Processing(CAPS) Cummins college of Engineering for Women, Pune.
  7. Khirade D and Patil B (2015) ‘plant disease detection using image processing’ International Conference on computing Communication and Control Automation, India pp 768-771.
  8. Panchal P, Raman C and Mantri S (2019) ‘plant disease detection and classification using machine learning models’ 2019; 4th International Conference on Computational Systems and information Technology for sustainable solution, India pp 1-6.
  9. Chapaneri R, Desai M, Ghose S and Das S ‘plant disease detection a comprehensive survey’ 2020; 3rd International Conference on Communication System Computing and IT applications; India; pp 220-225.