Horticulture: Smart trap for improved early detection of vine weevil to enable successful application of integrated pest management

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Currently the presence of vine weevil adults is often determined through visual plant assessments. However, this approach is time consuming, does not produce timely data and may damage crops so more effective monitoring methods are urgently needed. Most research in this area has focused on the use of artificial refuges or traps for adults, here referred to as monitoring tools, but these tools do not provide growers with timely or reliably data on the pests status of their crops. Existing monitoring tools exploit the negative phototaxis behaviour exhibited by adult vine weevil, which causes individuals to seek shelter during daylight. Designs such as grooved wooden boards placed on the ground, corrugated cardboard wrapped around plant stems or rolls placed on the ground are often used. A purpose-designed vine weevil monitoring tool is available from ChemTica (Costa Rica) and works on the same principle. A key reason why these monitoring tools do not work effectively is that little consideration has been given to the design and a semiochemical lure has not been developed for this pest. Recent work by members of this project team has demonstrated the importance of size, colour, shape and entrance number in determining in monitoring tool efficacy. Furthermore, it has been demonstrated that vine weevils aggregate and exhibit attraction to conspecifics, suggesting that potential insect derived semiochemical sources could be found in aggregation or trail-following pheromones. There is then the potential to optimise the design of a vine weevil monitoring tool and to develop and effective semiochemical lure in order to provide growers with a highly sensitive and reliable vine weevil monitoring tool. The monitoring tool can be further enhanced by incorporating the machine learning model that exploits key morphological features associated with vine weevil (e.g., extended snout and elbowed antennae) to classify individuals with an accuracy of 85 %.