Pests are major risk factors of crop production, their control makes a large share on production cost, in addition, chemical control methods raise environmental concerns and food security issues. The aim of the project is to develop a monitoring tool for vegetable crop production that allows targeted pest control. In order to reduce the dependence on the use of pesticides without profit loss, pest management decisions needs to be taken based on pest population estimations and models. Prevention of pest outbreaks are important, but if there is a need of pesticide spray, the timing of the application is critical for efficacy. For those pests which can not be trapped, the visual inspection by the farmer is the only way of forecasting. The project aims to develop a specific hand-device with camera that farmers can use to take pictures from the leaves and a software that recognizes and counts the pests on the leafs. The two targeted pest species, a whitefly (Trialeurodes vaporariorum) and a mite (Tetranychus urticae) have specific visual markers, that allows their identification by image analysis algorithms. These two pest species cause serious problems in high-value greenhouse crops like tomato, pepper, cucumber, cut flowers, etc.. Pest management decision is usually based on their population size that is estimated by field observation. A precise qualitative estimation is usually not possible, due to the small size of the pests. In addition, the sampling point with the picture will be marked by GPS. By taking several pictures in the field, a map of pest distribution will be created, that allows to identify hot spots of pests. Farmers will not need to spray the entire field, but can spray only the hot spots and prevent outbreaks by local treatments. Desk server application will be developed, data will be uploaded by the user to a desk server, where further data management application for long term planning and strategies web-client and sent to the webserver for further elaboration. The user will receive a map of the pest population. Archive data can be analysed to build more sustainable pest management strategies. This automatic ized pest-counting service is new to the market, currently there is no such device in use. The system, in addition, would be able to model pest populations on a map, since images are snet to the central server with geo-referenced parameters. With this technology, the severity of pest-infection in a labeled area can be mapped, and visualized on a smartphone. With this device, enhanced prognosis is created, localizing outbreaks, upgrading the use of chemicals and necessarily decreasing required quantity.