Banana Leaf Disease Images
Huge data is one of the required resources in deep learning research to train, validate and test CNN model and get better accuracy, prediction and detection. In our study we have prepared a banana plant leaf image dataset that is used for the ‘banana disease detection’ research. It is collected from Southern Nations, Nationalities, and Peoples' Regional State Arbaminch Zuria Woreda Lante kebele and Chano kebele and Gamugofa Zone Mierab Abaya Woreda Omolante kebele where banana is widely producing area and the infection of Xanthomonas wilt and Segatoka leaf spot disease is highly observed. The data is collected from four farmers a size of one hectare farm each in three kebeles. During the data collection, the daily collected data were identified “health” or “infected” by both type of diseases. The labeled data by the first plant pathologist is verified and confirmed by the second one to make sure the quality of the collected data. Finally, the collected image was correctly labeled with three classes. Collecting images of banana plant leaf in thousands is too difficult. The researcher collects 1,288 pictures of banana leaf under three categories as “Health” banana leaf, “Xanthomonas” infected leafs and “Sigatoka” infected leafs.
Steps to reproduce
The image of the leaf of the healthy and infected banana tree is collected using smart phone. Before beginning of collecting data, brief discussion with the two plant pathologist ware conducted on what is the symptom of the infected plant tree and how we can sure about the infection is Xanthomonas wilt disease or Segatoka infection.