A Novel Accurate Insecticidal Counting Method Based on Solar Insecticidal Lamp Using Machine Learning

Carrying out comprehensive green prevention and control on pests and plant diseases is important for improving quality of agricultural products, reducing usage of agricultural pesticides, and controlling losses. As an important approach of physical control, Solar Insecticidal Lamp has the counting function by killing pests during its working period, further obtaining the information of pest population density in the area covered by the SIL. Motivated by the current voltage recognition and counting technology, which cannot cope with the situation of killing a pest by continuous discharge, thus cannot provide an accurate insecticidal quantity, we proposed a novel accurate insecticidal counting method from the aspect of hardware and software. In the aspect of hardware, the SIL testbed is modified by changing high voltage package, and adding voltage comparator module and sound sensor module, further collecting the data Pulse Number of Insecticidal Discharges and Pulse Number of Insecticidal Sounds. In the aspect of software, we experimentally validated the relationship between PNID and PNIS as well as insecticidal quantity by seven different machine learning algorithms. Finally, we provide compelling experimental results and discuss strong evidence that points to a future need for lightweight edge computing on SIL, as a valuable consumer electronics.