Detection and Classification of Cassava Leaf Diseases using Squeezenet Pretrained Convolutional Neural Network and Support Vector Machine

Abstract

Cassava cultivation is important to the nation because of the products and raw materials that are supplied to industries. However, Its cultivation is affected by diseases such as cassava brown leaf and cassava mosaic, which have been classified using a Support Vector Machine (SVM). SqueezeNet pretrained Convolutional Neural Network (SPCNN) is adjudged a very effective image detection and classification algorithm with limited usage areas. This research, therefore was used to detect and classify cassava leaf diseases using SPCNN and SPCNN with multiclass SVM. Performance evaluation of both techniques showed that SPCNN effectively detected and classified cassava leaf diseases with optimal accuracies of 92.22% and 98.89% for SPCNN and SPCNN-SVM, respectively. Keywords- SqueezeNet, SVM, Cassava diseases, Feature extraction, Transfer learning Arinola I. O., Oke O. A. & Falohun A. S. (2026): Detection and Classification of Cassava Leaf Diseases using Squeezenet Pretrained Convolutional Neural Network and Support Vector Machine. Journal of Advances in Mathematical & Computational Science. Vol. 14, No. 1. Pp 1-12 Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V14N1P1