Abstract:

: Among the most common and serious tomato plant pests, leafminer flies ( Liriomyza sativae) are considered one of the major tomato-plant-damaging pests worldwide. Detecting the infestation and quantifying the severity of these pests are essential for reducing their outbreaks through effective management and ensuring successful tomato production. Traditionally, detection and quantification are performed manually in the field. This is time-consuming and leads to inaccurate plant protection management practices owing to the subjectivity of the evaluation process. Therefore, the objective of this study was to develop a machine learning model for the detection and automatic estimation of the severity of tomato leaf symptoms of leafminer fly attacks. The dataset used in the present study comprised images of pest symptoms on tomato leaves acquired under field conditions. Manual annotation was performed to classify the acquired images into three groups: background, tomato leaf, and leaf symptoms from leafminer flies. Three models and four different backbones were compared for a multiclass semantic segmentation task using accuracy, precision, recall, and intersection over union metrics. A comparison of the segmentation results revealed that the U-Net model with the Inceptionv3 backbone achieved the best results. For estimation of symptom severity, the best model was FPN with the ResNet34 and DenseNet121 backbones, which exhibited lower root mean square error values. The computational models used proved promising mainly because of their capacity to automatically segment small objects in images captured in the field under challenging lighting conditions and with complex backgrounds.

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