Cassava_Leaf_Detection

Cassava is a significant food security crop grown by smallholder farmers because it can tolerate harsh conditions. It is Africa’s second-largest producer of carbs. This starchy root is grown on at least 80% of home farms in Sub-Saharan Africa, although viral infections are a primary cause of low yields. It may be feasible to identify common diseases and cure them with the help of data science.Image classification methods based on convolutional neural networks have been grown rapidly given the growth of deep learning architecture and hardware computing capacity. In this study an empirical comparison is done to analyze the different model’s performance in terms of image classification. To identify cassava leaf diseases, both classic machine learning methods and deep learning models are used. The results shows that the overall accuracy of the model obtained is about 72% .Cassava is a significant food security crop grown by smallholder farmers because it can tolerate harsh conditions. It is Africa’s second-largest producer of carbs. This starchy root is grown on at least 80% of home farms in Sub-Saharan Africa, although viral infections are a primary cause of low yields. It may be feasible to identify common diseases and cure them with the help of data science.Image classification methods based on convolutional neural networks have been grown rapidly given the growth of deep learning architecture and hardware computing capacity. In this study an empirical comparison is done to analyze the different model’s performance in terms of image classification. To identify cassava leaf diseases, both classic machine learning methods and deep learning models are used. The results shows that the overall accuracy of the model obtained is about 72% .