T-CNN: Trilinear convolutional neural networks model for visual detection of plant diseases

Abstract

Plant diseases may threaten the safety of crops around the world, and timely detection of crop diseases and accurate determination of disease species are important to protect crop safety and control the spread of diseases. Recent studies have proposed the application of modern automatic recognition systems based on convolutional neural networks to the identification tasks of multiple crops and diseases. Although some research results have been achieved, studies have shown that these models are not optimal because they are susceptible to features unrelated to crop diseases, and have poor application ability in real-world environments. Therefore, this paper proposes a more concise method that separating the crop and disease identification and classify them independently, and demonstrates that it is more effective than the traditional crop-disease pairs approach. Meanwhile, we constructed a trilinear convolutional neural networks model using bilinear pooling and used images obtained in a real-world environment for the study of crop disease identification. The crop and disease identification accuracies achieved 99.99% and 99.7% on the test set in a controlled laboratory environment, and 84.11% and 75.58% on the test set in a real-world environment, respectively. The work in this paper improves the application value of crop disease identification research in the real-world.