Automated Real-Time Estimation of Severity and Early Diagnosis of Infestations Produced by Tetranychus spp. in Strawberry Crops

Avila C.; De-La-Cruz C.; Vilcarromero E.; Torres J.; Catano M. · IEEE Access (2026) · DOI: 10.1109/access.2026.3703228

Resumen

Tetranychus spp. is a prominent pest that affects strawberry cultivation, causing a significant decrease in economic yields. Manual inspection is the most common method used for identifying this pest, but it requires arduous labor, is slow, expensive, and inaccurate. This situation limits the ability to make an adequate early diagnosis of the damage caused by these spider mites. Thus, this paper presents a non-invasive method for real-time analysis of strawberry crops to determine the severity of Tetranychus spp. infestations. A field experiment was conducted to validate this systematic method through the biological characterization of spider mites, and a lightweight UNet-based convolutional neural network is proposed for the recognition of visual symptoms manifested on strawberry leaves containing Tetranychus spp. This proposed model uses a Residual bottleneck block to reduce the number of layers and network complexity, and an atrous spatial pyramid pooling algorithm with a custom kernel that improves accuracy for this application. A total of 2273 images with a resolution of 512× 512 pixels were collected from strawberry crops containing visible adaxial symptoms in complex backgrounds and were split into training (1772), validation (197), and test (304) datasets. The proposed model was evaluated alongside other state of the art semantic segmentation networks under the exact same training, validation, and testing conditions for a fair comparison. The results show that the proposed model achieves accuracy indicators comparable to the most recent semantic segmentation models, such as an IoU of 0.4791, but with twice the inference speed of the most accurate methods. Modifying the kernels for atrous convolutions demonstrated a substantial improvement in symptom recognition accuracy compared to standard atrous convolutions. Measurements of Lin’s Concordance Correlation Coefficient evidence that our model produces accurate severity estimations for a proper understanding of the actual degree of infestation present in crops. These outcomes evidence the value of the proposed algorithm to detect Tetranychus spp. in strawberry crop fields through real-time automated inspections.

Instituciones: Pontificia Universidad Católica del Perú