Sustainable control of infestations using image processing and modelling

A sustainable pest control system integrates automated pest detection and recognition to evaluate the pest density using image samples taken from habitats. Novel predator/prey modelling algorithms assess control requirements for the UAV system, which is designed to deliver measured quantities of naturally beneficial predators to combat pest infestations within economically acceptable timeframes. The integrated system will reduce the damaging effect of pests in an infested habitat to an economically acceptable level without the use of chemical pesticides. Plant pest recognition and detection is vital for food security, quality of life and a stable agricultural economy. The research utilises a combination of the k-means clustering algorithm and the correspondence filter to achieve pest detection and recognition. The detection is achieved by partitioning the data space into Voronoi cells, which tends to find clusters of comparable spatial extents, thereby separating the objects (pests) from the background (pest habitat). The detection is established by extracting the variant and distinctive attributes between the pest and its habitat (leaf, stem) and using the correspondence filter to identify the plant pests to obtain correlation peak values for the different datasets. The correspondence filter can achieve rotationally invariant recognition of pests for a full 360 degrees, which proves the effectiveness of the algorithm and provides a count of the number of pests in the image. A series of models has been produced that will permit an assessment of common pest infestation problems and estimate the number of predators that are required to control the problem within a time schedule. A UAV predator deployment system has been designed. The system is offered as a replacement for chemical pesticides to improve peoples’ health opportunities and the quality of food products.