Tomato Pest and Disease Identification Based on Improved Deep Residual Network and Transfer Learning

Peng Linli; Tjong Wan Sen; Hasanul Fahmi; Rusdianto Roestam

doi:10.24014/ijaidm.v8i1.34038

Tomato Pest and Disease Identification Based on Improved Deep Residual Network and Transfer Learning

Peng Linli, Tjong Wan Sen, Hasanul Fahmi, Rusdianto Roestam

Abstract

Tomatoes are a vital global crop, but their yield can be severely impacted by various diseases like leaf mold and spotted wilt. Early and accurate diagnosis of these diseases is crucial for implementing timely treatments, thereby reducing crop loss. Traditional manual diagnosis often suffers from low accuracy, high costs, and time consumption. To address these issues, this study introduces a method for identifying tomato pests and diseases using an improved residual network and transfer learning. A dataset comprising images of seven common tomato diseases and healthy leaves was created. This study introduces an improved residual network and transfer learning method to accurately identify tomato pests and diseases. The enhanced ResNet50 model, with an attention mechanism and focal loss, achieved 98.10% recognition accuracy. This research not only facilitates early disease detection, reducing crop loss but also minimizes pesticide use, thereby enhancing environmental sustainability and agricultural productivity worldwide.

Keywords

Attention Mechanism; DropBlock Regularization; Pests and Diseases; PlantVillage; ResNet50

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References

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DOI: http://dx.doi.org/10.24014/ijaidm.v8i1.34038

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