Automated Identification of Cotton Diseases and Pests
There is a constant need for optimizing food production and farming across the world, espe- cially in impoverished countries where the economy heavily depends on agriculture. The major contributing factors to crop yield loss are pest infestations and plant diseases. The U.N. Food and Agriculture Organization reports that 40% of crops are lost due to pests and diseases. This research project aims to contribute to the field of AI-based assistive technologies in precision agriculture by helping develop models to aid farmers in accurately identifying cotton pests and diseases from uploaded cotton leaf pictures. Farmers will be able to mitigate crop loss by (i) taking remedial ac- tion before additional damage occurs, (ii) minimizing pesticide waste by only spraying unhealthy crops, (iii) mapping areas of the field impacted by infestations, and (iv) reducing the health risks associated with the presence of excessive pesticides in the consumer’s food. However, training a high-quality model is challenging since real-world data contains more healthy leaves than diseased leaves and some plant diseases are more prevalent than others. This results in undesirable class im- balances and biases which lower the model’s accuracy. To address these imbalances, advanced data augmentation with generative adversarial networks (GANs) were used to create realistic-looking diseased plant images. These images were added to the underrepresented classes to balance the dataset and improve the model’s accuracy. Training high-quality GANs is computationally expen- sive. Each GAN took approximately 48 hours to train on a node with two V100 GPUs using TACC resources. Traditional affine data augmentation was also performed separately to compare with the GAN results.