Dengue case forecasting is important for the prevention and early control of outbreaks, as well as for the optimization of healthcare resources, among other aspects. This study addresses the need to develop increasingly accurate forecasting models that can support informed decision-making before and during dengue epidemics. Accordingly, two new models based on convolutional and recurrent neural networks, namely ConvLSTM and ConvBiLSTM, combined with data augmentation based on linear interpolation, are proposed. As a case study, weekly dengue cases in Peru from 2000 to 2024 are used. The proposed models are compared with well-known recurrent neural network-based models such as LSTM, BiLSTM, GRU, and BiGRU, both with and without data augmentation. The results show that the proposed models with data augmentation achieve comparable and superior performance to the benchmark models, while also exhibiting a lower average computational cost.

Monitoring PM2.5 in mining areas is essential for air quality management; however, most studies focus on single-step forecasts, limiting timely decision making. This work addresses the need for accurate multi-step PM2.5 prediction to support proactive pollution control in mining regions. So, a new model for multi-step PM2.5 time series forecasting is proposed, which is based on historical data such as the last day observed (LDO), retrieved data by correlation levels, and linear interpolation. As case studies, data from three environmental monitoring stations in mining areas of Peru were considered: Tala station near the Cuajone mine, Uchumayo near the Cerro Verde mine, and Espinar near the Tintaya mine. The proposed model was compared with benchmark models, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). The results show that the proposed model achieves results similar to those obtained by the benchmark models. The main advantages of the proposed model over the benchmark models lie in the amount of data required for predictions and the training time, which represents less than 0.2% of that required by deep learning-based models.

Global avocado production exceeds 10 million tons annually. Among the diseases affecting avocado fruit, anthracnose is one of the most significant, causing black lesions and fruit decay that can result in yield losses of 20–30%. To facilitate the early detection of anthracnose, this study proposes a computer vision-based approach. A dataset containing 2218 images of Fuerte avocados was first developed, comprising 1730 healthy samples and 488 anthracnose-infected samples after the labeling process. In the experimental phase, several convolutional neural network (CNN) models with varying depths (3, 4, 5, and 6 layers) were designed and evaluated. These models were subsequently integrated into different weighted ensemble configurations, where the best performance was achieved by the ensemble combining all four individual CNNs. The proposed weighted ensemble was compared against widely used state-of-the-art architectures, including VGG-16, ResNet-18, and MobileNetV2. Experimental results demonstrated the effectiveness of the proposed approach, achieving an F1-score of 0.9052, outperforming VGG-16 (0.8283), ResNet-18 (0.7328), and MobileNetV2 (0.7320).

Ring-neck disease in avocado cultivation is a critical issue due to its significant impact on production, including economic losses caused by premature fruit drop, postharvest rejection, and export limitations, among other factors. To enable the automated detection of ring-neck symptoms, this study proposes a computer vision-based framework. As an initial step, a dataset of 622 Fuerte avocado images was established and annotated, consisting of 361 healthy samples and 261 samples affected by ring-neck. During the experimental stage, convolutional neural network (CNN) models with different architectures, comprising four and five convolutional layers, were developed and systematically evaluated. In addition, a region-of-interest (ROI)-based strategy was employed to focus the analysis on the fruit peduncle, thereby enhancing the detection of ring-neck symptoms. The performance of the proposed model was benchmarked against widely adopted deep learning architectures, namely VGG-16, VGG-19, ResNet-18, MobileNetV3, and GhostNet. The experimental evaluation demonstrated the superiority of the proposed approach, achieving an F1-score of 0.9610, compared with 0.5667 for VGG-16, 0.7164 for VGG-19, 0.0952 for ResNet-18, 0.6176 for MobileNetV3, and 0.7733 for GhostNet. Furthermore, statistical significance was assessed using the McNemar test, which confirmed that the observed performance improvements over the benchmark models were statistically significant, thereby supporting the robustness and reliability of the proposed approach.