Abstract
The Australian and global agricultural industries are experiencing economic challenges. These challenges to farming are caused by a group of related factors including lack of labour supply, low profit margins, and high operational costs. Of all the factors impacting upon the operation and profitability of farms, it is human labour and chemical pesticides which are most effectual. One potential solution which has been proposed to reduce the impact of said factors is the use of robotic labour on farms. However, the available robotic crop disease classification models are lacking in accuracy and efficiency for this solution to be commercially viable. Therefore, this research aims to further understand what progress needs to be made in order to increase the accuracy and efficiency of crop disease classification models, specifically for disease species classification and disease severity classification purposes.
As part of this research, previous literature will be examined to determine the current scope of automatic intelligent crop disease classification capabilities. Subsequently, three State-of-the-Art (SotA) image classification models (AlexNet, MobileNetv3, and MLP-Mixer) will be trained and tested to determine the current standard of performance for crop disease species classification. Subsequently, this research will determine, based upon model performance of currently existing models, which model architecture produces favourable results for crop disease severity classification.
