Deep Learning-Based Multi-Class Pest and Disease Detection in Agricultural Fields

Farmers and agricultural workers would manually inspect crops for signs of pests or use traps to monitor pest populations. The advent of deep learning algorithms such as vision transformers and FastAI ResNet has brought about a significant transformation in pest detection practices. These advanced algorithms leverage the capabilities of artificial intelligence to process vast amounts of data and learn intricate patterns associated with different pest species and their impact on crops. Unlike manual methods, deep learning algorithms can analyze large datasets quickly and accurately, leading to more efficient and effective pest detection. Vision transformers and FastAI ResNet stand out for their ability to continuously learn and adapt to new data, including changes in pest populations over time. This adaptability is crucial in agriculture, where pest dynamics can vary due to factors like climate conditions, environmental changes, and pest control interventions. FastAI ResNet-50 and Vision Transformers have demonstrated remarkable accuracy in classifying various disease classes, indicating their reliability and precision in detecting different pests and diseases affecting crops. Their high accuracies, ranging from 0.95 to 1.00, underscore their effectiveness in agricultural pest detection tasks. However, the study highlights challenges that arise when dealing with more classes in a classification task. Factors such as increased complexity, imbalanced data distributions, and higher-dimensional feature spaces can impact model accuracy. To address these challenges, the study recommends various strategies, including data augmentation, class balancing, robust model architectures, regularization techniques, and transfer learning. Implementing these strategies can help maintain or improve accuracy levels, ensuring that deep learning models remain effective and reliable for agricultural pest detection and disease management applications.