Abstract

Plant disease identification in Sub-Saharan Africa poses a significant challenge, hindered by costly laboratory tests or subjective visual assessments. Recent advances in image-based disease identification show promise, but existing methods are limited in accuracy and efficiency. This study addresses these shortcomings by presenting a convolutional neural network (CNN)-based plant disease classifier, leveraging transfer learning from pre-trained models Xception, MobileNetV2, and InceptionV3. A high generalization rate of 98.76% is achieved in the test data, demonstrating the potential for efficient and accurate identification of plant disease. This research contributes to innovative agricultural management solutions in Sub-Saharan Africa, with implications for improving crop yields, food security, and sustainable agriculture.

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