Rice paddy disease classifications using CNNs
This work addresses crop yield losses for farmers and agricultural stakeholders, but it is incremental as it extends previous modeling efforts.
The authors tackled the problem of rice paddy disease classification by analyzing how accuracy depends on model architecture and computer vision techniques, achieving 98.7% accuracy for 10 disease classes, an improvement over previous state-of-the-art models with 93% accuracy for 5 diseases.
Rice is a staple food in the world's diet, and yet huge percentages of crop yields are lost each year to disease. To combat this problem, people have been searching for ways to automate disease diagnosis. Here, we extend on previous modelling work by analysing how disease-classification accuracy is sensitive to both model architecture and common computer vision techniques. In doing so, we maximise accuracy whilst working in the constraints of smaller model sizes, minimum GPUs and shorter training times. Whilst previous state-of-the-art models had 93% accuracy only predicting 5 diseases, we improve this to 98.7% using 10 disease classes.