Apple is one of the most important economic fruit crops in the world. Despite all the strategies of integrated pest management (IPM), insecticides are still frequently used in its cultivation. In addition, pest phenology is extremely influenced by changing climatic conditions. The frequent spread of invasive species, unexpected pest outbreaks, and the development of additional generations are some of the problems posed by climate change. The adopted strategies of IPM therefore need to be changed as do the current monitoring techniques, which are increasingly unreliable and outdated. The need for more sophisticated, accurate, and efficient monitoring techniques is leading to increasing development of automated pest monitoring systems. In this paper, we summarize the automatic methods (image analysis systems, smart traps, sensors, decision support systems, etc.) used to monitor the major pest in apple production (Cydia pomonella L.) and other important apple pests (Leucoptera maifoliella Costa, Grapholita molesta Busck, Halyomorpha halys Stål, and fruit flies—Tephritidae and Drosophilidae) to improve sustainable pest management under frequently changing climatic conditions.

Deep neural networks (DNNs) have recently been applied in many areas of agriculture, including pest monitoring. The codling moth is the most damaging apple pest, and the currently available methods for its monitoring are outdated and time-consuming. Therefore, the aim of this study was to develop an automatic monitoring system for codling moth based on DNNs. The system consists of a smart trap and an analytical model. The smart trap enables data processing on-site and does not send the whole image to the user but only the detection results. Therefore, it does not consume much energy and is suitable for rural areas. For model development, a dataset of 430 sticky pad photos of codling moth was collected in three apple orchards. The photos were labelled, resulting in 8142 annotations of codling moths, 5458 of other insects, and 8177 of other objects. The results were statistically evaluated using the confusion matrix, and the developed model showed an accuracy > of 99% in detecting codling moths. This developed system contributes to automatic pest monitoring and sustainable apple production.

The brown marmorated stink bug Halyomorpha halys (Stål, 1855), native to East Asia, is an extremely polyphagous pest that infests more than 300 plant species from 49 families. In Europe and North America, this pest causes enormous damage to the production of economically important crops (tree fruit, vegetables, field crops, and ornamental plants). Global warming favours its spread, as the rise in temperature results in the appearance of further generations of the pest. Halyomorpha halys (nymph and adult) causes damage typical of the Pentatomidae family by attacking host plants throughout their development (buds, stems, fruits, and pods). Ripe fruits are often disfigured, and later suberification and necrotic spots form on the fruit surface, making them accessible to plant pathogens that cause fruit rot and rendering them unmarketable. The increasing global importance of the pest suggests that more coordinated measures are needed to contain its spread. Understanding the biology and ecology of this species is crucial for the development of reliable monitoring and management strategies. Most insecticides available for the control of H. halys have a broad spectrum of modes of action and are not compatible with most integrated pest management systems, so biological control by natural enemies has recently been emphasised. Preventing excessive population growth requires early identification and effective control measures that can be developed quickly and applied rapidly while respecting the environment. This paper presents a comprehensive review of the latest findings on the global distribution of this important pest, its potential spread, biology and ecology, key host plants of economic importance, monitoring methods, and effective biological control strategies, as well as future perspectives for sustainable H. halys control measures.

The pear leaf blister moth is a significant pest in apple orchards. It causes damage to apple leaves by forming circular mines. Its control depends on monitoring two events: the flight of the first generation and the development of mines up to 2 mm in size. Therefore, the aim of this study was to develop two models using artificial neural networks (ANNs) and two monitoring devices with cameras for the early detection of L. malifoliella (Pest Monitoring Device) and its mines on apple leaves (Vegetation Monitoring Device). To train the ANNs, 400 photos were collected and processed. There were 4700 annotations of L. malifoliella and 1880 annotations of mines. The results were processed using a confusion matrix. The accuracy of the model for the Pest Monitoring Device (camera in trap) was more than 98%, while the accuracy of the model for the Vegetation Monitoring Device (camera for damage) was more than 94%, all other parameters of the model were also satisfactory. The use of this comprehensive system allows reliable monitoring of pests and their damage in real-time, leading to targeted pest control, reduction in pesticide residues, and a lower ecological footprint. Furthermore, it could be adopted for monitoring other Lepidopteran pests in crop production.

The brown marmorated stink bug, Halyomorpha halys Stål, is an invasive Pentatomidae species that causes damage to economically important agricultural crops. It originated in East Asia but has successfully spread to many European countries. It has been present in Croatia since 2017, and its first mass emergence was detected in a soybean field in 2019. Therefore, the aim of this study was to determine the occurrence and population size of H. halys in perennial crops in important fruit growing areas in Croatia. According to the monitoring data, H. halys was detected in all surveyed areas and crops (apple, apricot, cherry, fig, grapevine, pear, plum, olive, and hazelnut). The largest population size was detected for hazelnut in the locality of Tovarnik, Vukovar-Syrmia County (79 individuals), for pear in the locality of Marijanci, OsjekBaranja County (31 individuals), and for apple in the locality of Staro Čiče, Zagreb County (25 individuals). In view of the fact that the weekly catches at some monitoring sites exceeded the established treatment threshold, control measures must be taken to prevent an increase in the population and significant damage in the future. In addition to hazelnuts, crops such as pears, apples and cherries are particularly threatened by this pest.

Prays oleae Bernard, 1788, or the olive moth, is a significant pest in Croatian olive groves. This study aims to develop a functional model based on an artificial neural network to detect olive moths in real time. This study was conducted in two different orchards in Zadar County, Croatia, in the periods from April to September 2022 and from May to July 2023. Moth samples were collected by placing traps with adhesive pads in these orchards. Photos of the pads were taken every week and were later annotated and used to develop the dataset for the artificial neural network. This study primarily focused on the average precision parameter to evaluate the model’s detection capabilities. The average AP value for all classes was 0.48, while the average AP value for the Olive_trap_moth class, which detected adult P. oleae, was 0.59. The model showed the best results at an IoU threshold of 50%, achieving an AP50 value of 0.75. The AP75 value was 0.56 at an IoU = 75%. The mean average precision (mAP) was 0.48. This model is a promising tool for P. oleae detection; however, further research is advised.