Pest Detection in Agricultural Farms using SqueezeNet and Multi-Layer Perceptron Model

Pest detection is essential to protect agricultural systems from economic losses, lower food production, and environmental degradation. Detection of pests is a crucial aspect of agricultural sustainability because it helps to allocate resources, reduce production costs, and increase producers' profits. Artificial intelligence (AI) has revolutionized the detection of agronomic pests by employing deep learning models to accurately detect individual pests and differentiate between species and life stages. Combining SeueezeNet and Multi-Layer Perceptron, this study extracts feature vectors from image data to detect pests. There are four primary phases: preprocessing, image embedding with SqueezeNet, the final classifier with MLP, and 10-fold cross-validation. Data for this study is acquired in the form of plant pests. The total number of images acquired is 3150, with 350 from each class. Based on the research, the combination model demonstrates excellent performance. Each experiment's accuracy is greater than 99 %. It shows that Squeezenet can effectively extract the data's features, whereas Multi-Layer Perceptron can process these features for optimal classification performance. Even though there are still several classes, such as mites, sawflies, and stem borer, that have not been correctly classified. Since each image's background is unique, it cannot be classified correctly. These promising findings have broad implications for boosting agricultural output and decreasing pest-related losses. Optimal use of this approach in a variety of agricultural contexts requires more study and field testing.

@article{Yulita2024,
  title     = {Pest Detection in Agricultural Farms using SqueezeNet and Multi-Layer Perceptron Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Intan Nurma Yulita and Anton Satria Prabuwono and Firman Ardiansyah and Juli Rejito and Asep Sholahuddin and Rudi Rosadi},
  doi       = {10.14569/IJACSA.2024.0150680},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150680}
}