South American tomato leaf miner Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) is responsible for significant biological invasions of tomato crops, perturbing global food production. The larvae feed within leaves, stems, and fruits, creating characteristic mines that reduce photosynthetic capacity by causing leaf necrosis and rendering fruits unmarketable. Synthetic pesticide applications used to suppress pest populations have detrimental impacts on the environment and human health and incur high production costs to farmers. These prevailing situations highlight the urgency of sustainable pest management practices. AI-driven pest monitoring systems assist farmers by enabling early pest detection and predicting expected pest outbreaks before their devastating attacks on crops. These data-driven insights facilitate farmers in implementing proactive pest control strategies, permitting reduced but impactful pesticide use through tracking pest population levels in the field. To develop smart pest monitoring systems to control T. absoluta outbreaks in tomato crops in greenhouse settings, this study highlights the use of AI-based pest detection and population prediction models to detect pest incidences and predict outbreaks in advance. A real-time image dataset was collected in greenhouse conditions to train various object detection models such as YOLOv10, YOLOv11, and YOLOv26 for the early detection of T. absoluta. The YOLOv26 model outperformed its counterpart approaches in terms of pest detection accuracy and inference speed by delivering 98.4% (mAP50) and 0.053 seconds, respectively. For early prediction of pest outbreaks, abiotic parameter data were collected through IoT sensors alongside pest biological data to generate early forecasts. Feature engineering and feature selection approaches were used for data preparation to model pest population dynamics. Various ML and ensemble learning approaches were implemented to model pest dynamics in relation to different meteorological parameters, where a blended ensemble learning technique resulted in the best prediction performance by delivering 4.99517, 62.415492, and 7.900348 values for MAE, MSE, and RMSE metrics, respectively.

Artificial intelligence provides a practical framework for crop damage assessment from imagery data, supporting early decision-making in agricultural management. In peach orchards, climate change increases abiotic stress and biotic pressures, including pests and diseases, which often produce visually similar foliar symptoms. This overlap makes manual diagnosis difficult, especially across multiple fields with varying environmental conditions, highlighting the need for automated models with strong generalization ability. We propose an image-based classification approach for peach leaf damage detection. A benchmark dataset was created through manual annotation of publicly available images, consisting of 1,366 peach leaves across six damage categories. Several deep learning architectures were evaluated. EfficientNet models achieved the best results, with EfficientNetB0 reaching 92.9 percent accuracy, EfficientNetB3 achieving 91.5 percent, and EfficientNetB5 showing the strongest performance on minority classes. DenseNet121 reached 92.6 percent accuracy. The integration of the Convolutional Block Attention Module (CBAM) improved performance in several backbones, particularly EfficientNetB5 and InceptionV3, while showing limited or negative impact in others. The CBAM-enhanced EfficientNetB5 achieved the best overall accuracy of 93.3 percent. To evaluate robustness under realistic conditions, a local dataset of 180 images across four classes was collected, and transfer learning strategies were applied to address domain shift. Three fine-tuning strategies were tested. EfficientNetB3 combined with CBAM achieved the best performance in the local domain, reaching a 93 percent macro F1-score after transfer. Overall, attention-based models showed improved robustness for minority classes and better generalization across different field conditions.

South American tomato leaf miner Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) is responsible for significant biological invasions of tomato crops, perturbing global food production. To develop smart pest monitoring systems to control T. absoluta populations in tomato crops in greenhouse settings, this study highlights the use of AI-based pest detection and population prediction models to detect pest instances and forecast populations in advance. A real-time image dataset was collected in greenhouse conditions to train various object detection models such as YOLOv10, YOLOv11, and YOLOv26 for the early detection of T. absoluta. A dataset of 317 trap images was deployed to evaluate the detection capabilities of pest detection models. The YOLOv26s model outperformed its counterpart approaches in terms of pest detection accuracy by delivering 92.4% (mAP₅₀), and YOLOv26n delivered an inference speed of 0.064 s on test data. For early prediction of the male moth population, abiotic parameter data were collected through IoT sensors alongside pest biological data to generate early forecasts. Feature engineering and feature selection approaches were used for data preparation to model male moth population dynamics. Machine learning approaches and a hybrid linear-tree stacking framework were implemented to model pest dynamics in relation to different meteorological parameters. The hybrid stacking model resulted in an NRMSE value of 0.31 when predicting the one-week-ahead population of male T. absoluta.