The Power of Vision Transformers and Acoustic Sensors for Cotton Pest Detection

Abstract

Whitefly infestations have posed a severe threats to cotton crops in recent years, affecting farmers globally. These little insects consume food on cotton plants, causing leaf damage and lower crop yields. In response to this agricultural dilemma, we developed a novel method for detecting whitefly infestations in cotton fields. To improve pest detection accuracy, we use the combined efficiency of visual transformers and low-cost acoustic sensors. We train the vision transformer with a large dataset of cotton fields with and without whitefly infestations. Our studies yielded encouraging results, with the vision transformer obtaining an amazing 99% accuracy. Surprisingly, this high degree of accuracy is reached after only 10-20 training epochs, outperforming benchmark approaches, which normally give accuracies ranging from 80% to 90%. These outcomes underline the cost-effective potential of the vision transformer in detecting whitefly attacks on cotton crops. Moreover, the successful integration of acoustic sensors and vision transformers opens doors for further research and advancements in the domain of cotton pest detection, promising more robust and efficient solutions for farmers facing the challenges of whitefly infestations.

Keywords

• Acoustic sensors
• vision transformer
• neural network
• VGG16
• cotton white fly attack