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of IPM therefore need to be changed as do the current monitoring techniques, which are increasingly unreliable and outdated.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.

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.

Spotted wing drosophila (Drosophila suzukii (Matsumura, 1931)) a polyphagous alien invasive species causes economic damages in cultivation of soft fruits all over the word. It is widespread in Croatia and considering that the economic damage occurred in greenhouse cultivation of soft fruit several years ago, new damage in this production can be expected. The pest development was monitored on 50 overripe fruits of cultivars 'Amira' and 'Sugana' cultivated in greenhouses in Zagreb in 2018 to investigate pest preference for these cultivars and to make a risk assessment in raspberry cultivation. Pest presence was recorded on both cultivars at the same time, and D. suzukii was dominant drosophilid species in development. Significantly more drosophilids as well as individuals of D. suzukii were developed on cultivar 'Amira'. On 'Amira' 373 female and 211 male of D. suzukii developed, while on 'Sugana' 253 female and 142 males developed. Average number of pests per fruit on 'Amira' counted 11.68 and on 'Sugana' 7.9. Drosophila suzukii develops in high populations in the greenhouse production of raspberry cultivars, which poses a serious risk for their cultivation in the study site.

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.