Developing of CNN Model for Disease Detection on Cassava Leaves Using VGG-16 Algorithm


  • B. Deva Harsha, Shaik Khajavali, M. L. Sneha Snigdha, Polagani Roshini, Bandlamudi Srilakshmi, Arepalli Gopi


Cassava Leaves, Disease Detection, Identification, Performance, Accuracy.


Sub-Saharan Africa is home to many important crops, one of which being cassava. For many people, it is their staple meal. Although cassava leaves are full of health advantages, the illnesses that have been affecting it, have caused a significant reduction in productivity. The lab testing may need more time and resources from cultivators than they have. In order to meet these challenges, farmers therefore require a fast and efficient problem identification approach. In an effort to maximize model performance, the offered deep learning model utilizes the advantages of the EfficientNet-B0 architecture, which has been enhanced with k-fold cross-validation. The primary objective of the research is to use picture classification to precisely detect the illnesses that specifically affect cassava plants via deep learning. Early intervention steps, such as the targeted use of pesticides or the quarantine of infected crops, may be made feasible by this identification. Every one of testing and training image originates from a natural environment in a farming region. To determine the model's authentic outcomes, it has been validated by employing a particular set of data. To sum up, this study promotes the practices of agriculture and food security by utilizing deep learning techniques to fight Cassava infections. The resilience of the cassava crop may be substantially improved through the establishment of an accurate disease identification and prevention model, which will eventually enhance food production and the daily lives of those who depend on this important commodity.


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How to Cite

B. Deva Harsha. (2024). Developing of CNN Model for Disease Detection on Cassava Leaves Using VGG-16 Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2734–2747. Retrieved from



Research Article