Pruning is a common practice in citrus for various reasons. These include controlling and shaping the canopy; improving phytosanitary health, productivity, and fruit quality; and facilitating operations such as harvesting and phytosanitary treatments. Because pruning is an expensive operation, its need is sometimes questioned. However, it has been proven to be particularly important in Mediterranean citriculture, which is oriented towards producing fruits for a high-quality demanding fresh market. Herein, we summarize and explain the pruning techniques used in Mediterranean citriculture and refer to the main purposes of each pruning type, considering citrus morphology and physiology.

Industrial maintenance has shifted from reactive repairs and calendar-based servicing toward data-driven predictive strategies. This paper presents a non-intrusive, low-cost IoT hardware platform for sustainable predictive maintenance of rotating machinery. The system integrates an ESP32-S3 sensor node that captures vibration (100 kHz) and temperature data, performs local logging, and communicates wirelessly. An automated spectral band segmentation framework is introduced, comparing equal-energy, linear-width, nonlinear, clustering, and peak–valley partitioning methods, followed by a weighted feature scheme that emphasizes high-value bands. Three unsupervised one-class classifiers—transformer autoencoders, GANomaly, and Isolation Forest—are evaluated on these weighted spectral features. Experiments conducted on a custom pump test bench with controlled anomaly severities demonstrate strong anomaly classification performance across multiple configurations, supported by detailed threshold-characterization metrics. Among 150 model–segmentation configurations, 25 achieved perfect classification (100% precision, recall, and F1 score) with ROC-AUC = 1.0, 43 configurations achieved ≥90% accuracy, and the lowest-performing setup maintained 81.8% accuracy. The proposed end-to-end solution reduces the downtime, lowers maintenance costs, and extends the asset life, offering a scalable, predictive maintenance approach for diverse industrial settings.