Wood-boring insect pests pose a significant threat to orchards, potentially leading to tree mortality. In the initial stages of infestation, no visible symptoms are apparent, but as infestations progress, rapid and widespread symptoms emerge, resulting in accelerated tree decline. Therefore, the timely detection of early wood-boring insect symptoms is critical for effective pest control, necessitating advanced methods such as remote sensing. In this study, remote sensing is utilized to identify the early symptoms of peach flatheaded root borer (PFRB) infestation in trees. A multispectral sensor attached to a UAV captures aerial imagery data from stone fruit and pome fruit orchards. These data undergo processing in photogrammetric and GIS programs, where NDVI, NDRE, and the tree crown area are computed. On-site observations confirm PFRB infestations. Various machine-learning models, including logistic regression (LR), artificial neural network (NN), random forest (RF), and extreme gradient boosting (XGBoost), are compared using mean NDVI values, mean NDRE values, crown area, mean temperature, and mean relative humidity. Mean NDVI values emerge as the most crucial factor for predicting PFRB infestation across all machine-learning models. The XGBoost model proves the most effective, achieving an accuracy of 0.85, with marginal variations from the other tested models.

The tiger longicorn beetle, Xylotrechus chinensis Chevrolat (Coleoptera: Cerambycidae), has posed a significant threat to mulberry trees in Greece since its invasion in 2017, which may be associated with global warming. Detection typically relies on observing adult emergence holes on the bark or dried branches, indicating severe damage. Addressing pest threats linked to global warming requires efficient, targeted solutions. Remote sensing provides valuable, swift information on vegetation health, and combining these data with machine learning techniques enables early detection of pest infestations. This study utilized airborne multispectral data to detect infestations by X. chinensis in mulberry trees. Variables such as mean NDVI, mean NDRE, mean EVI, and tree crown area were calculated and used in machine learning models, alongside data on adult emergence holes and temperature. Trees were classified into two categories, infested and healthy, based on X. chinensis infestation. Evaluated models included Random Forest, Decision Tree, Gradient Boosting, Multi-Layer Perceptron, K-Nearest Neighbors, and Naïve Bayes. Random Forest proved to be the most effective predictive model, achieving the highest scores in accuracy (0.86), precision (0.84), recall (0.81), and F-score (0.82), with Gradient Boosting performing slightly lower. This study highlights the potential of combining remote sensing and machine learning for early pest detection, promoting timely interventions, and reducing environmental impacts.

Locusts represent a persistent global agricultural pest, responsible for significant crop losses and socio-economic repercussions. The initiation of chemical control measures dates back to the late 19th century, with the use of poisoned baits, before advancing in the mid-20th century with the introduction of organochlorines, such as dieldrin. Despite their efficacy, the associated environmental, ecological, and human health risks led to the prohibition of dieldrin by the United States and the FAO by 1988. The demand for insecticides with reduced persistence and toxicity prompted the establishment of international organizations to coordinate locust research and management. In recent decades, chemical control has transitioned towards compounds with diminished persistence and selective agents. Concurrently, research has progressed in the development of bioinsecticides, notably Metarhizium acridum, and has reinforced preventive strategies. Emerging technologies, including remote sensing and machine learning, have facilitated early monitoring and predictive modeling, thereby enhancing outbreak forecasting. These tools support proactive, targeted interventions and are consistent with Integrated Pest Management principles, promoting more sustainable and ecologically responsible locust control strategies.

The efficacy of entomopathogenic nematodes (EPNs), Steinernema feltiae and Heterorhabditis bacteriophora, for controlling Frankliniella occidentalis (Thysanoptera: Thripidae, Pergande, 1895) in nectarine orchards, was evaluated, alongside their potential impact on honeybees (Apis mellifera, Hymenoptera: Apidae, Linnaeus, 1785). Field trials revealed that S. feltiae significantly reduced thrips populations and fruit damage compared to the control, while H. bacteriophora showed no significant effect. The reduction in feeding scars on fruits from S. feltiae–treated trees underscores its potential as a sustainable biocontrol agent. However, laboratory bioassays indicated that another EPN, Steinernema carpocapsae, induced delayed mortality in honeybees under high-exposure conditions, pinpointing the need for careful application to minimize risks to pollinators. These findings demonstrate the potential of S. feltiae for integrated pest management (IPM) in nectarine cultivation while emphasizing the importance of species-specific selection and pollinator safety. Future research should focus on optimizing application methods, understanding environmental influences on EPN efficacy, and assessing long-term impacts on pollinator health to ensure sustainable pest management practices.