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.

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.

Resistance development in Bemisia tabaci (Homoptera: Aleyrodidae) against a score of synthetic chemicals necessitates the exploration of novel eco-friendly integrated pest management approaches to protect field crops. In the current study, the pathogenic potential of two EPFs i.e. Beauveria bassiana and Isaria fumosorosea and two plant extracts viz. Neem leaves extract and Aloe vera leaves extract was assessed against eggs, nymphs, and adults of B. tabaci on cotton plants. Five different concentrations of EPFs (1×104, 1×105, 1×106, 1×107 and 1×108 conidia/mL) and plant extracts (10 %, 20 %, 40 %, 60 %, and 80 %) were assessed against eggs, nymphs and adults of B. tabaci. The results revealed that eggs, nymphs and adults of B. tabaci were significantly affected in terms of mortality by the tested biopesticides. Furthermore, mortality response of immatures and adults of B. tabaci was more pronounced at higher treatment concentrations and post-inoculation time intervals. Among EPFs, B. bassina was more pathogenic towards B. tabaci in comparison to its counterpart, I. fumosorosea. Similarly, Neem extracts exhibited higher mortalities as compared to Aloe vera extract. Taken together, EPFs and botanicals can provide eco-friendly and sustainable solution to manage B. tabaci. However, future studies need to conducted to find their recommended doses in field conditions.

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.