Asian citrus psyllid, Diaphorina citri Kuwayama (Liviidae: Hemiptera) is a menacing and notorious pest of citrus plants. It vectors a phloem vessel‐dwelling bacterium Candidatus Liberibacter asiaticus, which is a causative pathogen of the serious citrus disease known as Huanglongbing. Huanglongbing disease is a major bottleneck in the export of citrus fruits from Pakistan. It is being responsible for huge citrus economic losses globally. In the current study, several prediction models were developed based on regression algorithms of machine learning to monitor different phenological stages of Asian citrus psyllid to predict its population about different abiotic variables (average maximum temperature, average minimum temperature, average weekly temperature, average weekly relative humidity, and average weekly rainfall) and biotic variable (host plant phenological patterns) in citrus‐growing regions of Pakistan. The pest prediction models can be used for proper applications of pesticides only when needed for reducing the environmental and cost impacts of pesticides. Pearson’s correlation analysis was performed to find the relationship between different predictor (abiotic and biotic) variables and pest infestation rate on citrus plants. Multiple linear regression, random forest regressor, and deep neural network approaches were compared to predict population dynamics of Asian citrus psyllid. In comparison with other regression techniques, a deep neural network‐based prediction model resulted in the least root mean squared error values while predicting egg, nymph, and adult populations.

Global climate change and increased anthropogenic activities are responsible for outbreaks of invasive and endemic pests, diseases, and pathogens. Forest pests pose serious threats to the growth, productivity, and resilience of natural forests and green urban spaces globally. To cope with the hazardous impacts of trunk-boring pests on forest degradation, it is crucial to detect their infestations at early growth stages. However, the borer larvae’s hidden lifestyle and delayed apparent symptoms lead to widespread infestation, resulting in large-scale tree mortality. The development of smart systems is critical to protecting natural forests and forest plantations from the fatal impacts of boring pests. Applications of artificial intelligence (AI), Internet of Things (IoT), and remote sensing (RS) technologies are transforming traditional pest management strategies by developing more sustainable and robust solutions. The integration of these cutting-edge technologies is paving the way for early pest detection, identification, and outbreak prediction by implementing on-site decision support systems (DSS) and remote monitoring of agricultural fields and large-scale forests. Advanced IoT acoustic sensors can record vibrational signals of boring pest larvae activities inside the tree trunk to confirm pest infestations at early stages, assisting forest management stakeholders in taking preventive measures to suppress the pests’ outbreaks in a timely manner. Auditory signals recorded by piezoelectric sensors are affected by external environmental noise. To improve pest detection ability, different AI-based signal enhancement algorithms are deployed to suppress noisy signals and process enhanced signals. Enormously improved RS technologies can monitor dynamic structural changes in forests by capturing multispectral images through satellites and drones equipped with high-resolution cameras, and then the RS data are incorporated into advanced AI algorithms to analyze the pests and disease-induced stress in the forest ecosystem to develop smart forest protection systems. The integration of X-ray imaging and deep learning models is another achievement in the non-destructive management of trunk-boring pests.

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.

The Asian Citrus Psyllid(ACP) is the most serious pest of citrus, causing severe economic losses to the citrus industry globally. To mitigate the gravest impacts of ACP on the citrus industry, pest-biological control methods are of utmost importance to reduce their population naturally. The ACP-Predators dataset comprises three predatory species: Chrysopids, Coccinellids, and Spiders’ data, considered as beneficial insects to suppress ACP populations. The tap-sampling method was used to collect insect count data from two study locations and two citrus species. Meteorological variables and hostplant phenological patterns data were collected alongside data on beneficial insects to study the impact of these parameters on the population dynamics of certain predatory species. The important independent variables of this dataset are Average Max Temp, Average Min Temp, Average Weekly Temp, Average Weekly % Relative Humidity, Average Weekly Rainfall, and the biotic variable (% Branches with Flush), and the dependent variables are weekly counts of Spiders, Coccinellids, and Chyrysopids for each study site and citrus species. The dataset has the potential to be reused in entomology and ecology to study the interactions between meteorological parameters and the population dynamics of beneficial insect species. These insights can assist farmers in making informed decisions regarding pesticide application while considering the population of natural enemies in the field. This dataset can be used in the Artificial Intelligence domain to develop smart pest management systems by combining ACP, predator, and parasitoid data with various biotic and abiotic parameters.