A Delivery System Embedded Trap Device for Attracting Female and Male Fruit Flies is disclosed in the present invention which can attract both female and male of fruit flies for an extended period of 180 days irrespective of the season thereby effectively controlling the major pest in agriculture.

A light source device for trapping indoor adult Phycitinae, which is an adult moth belonging to subfamily Phycitinae, is configured to: emit an attracting light at a predetermined photon flux density; and form a guide path by the attracting light to guide the adult Phycitinae to a vicinity of an emission end of the attracting light in a region having a photon flux density lower than the predetermined photon flux density on a side lower than a height of the emission end of the attracting light

An insect monitoring system (ISM) and related e-commerce system are provided. The ISM includes an insect attracting light, an intake, an exhaust, an airflow conduit between intake and exhaust, an insect collecting mesh in the conduit, a fan for generating airflow in the airflow conduit, and one or more cameras pointed on the insect collecting mesh. The insect attracting light and the fan are activated such that insects are drawn into the intake and trapped against the insect collecting mesh. The images of the insect collecting mesh are processed and analyzed using a machine learning algorithm to recognize a type and number of insects. Recommendations are provided based on the analysis. In some aspects, the insect monitoring system is connected to electronic client devices over a communication network. The electronic devices are provided with an e-commerce application for sale of products and services responsive to the recommendations.

An insect monitoring system (ISM) and related e-commerce system are provided. The ISM includes an insect attracting light, an intake, an exhaust, an airflow conduit between intake and exhaust, an insect collecting mesh in the conduit, a fan for generating airflow in the airflow conduit, and one or more cameras pointed on the insect collecting mesh. The insect attracting light and the fan are activated such that insects are drawn into the intake and trapped against the insect collecting mesh. The images of the insect collecting mesh are processed and analyzed using a machine learning algorithm to recognize a type and number of insects. Recommendations are provided based on the analysis. In some aspects, the insect monitoring system is connected to electronic client devices over a communication network. The electronic devices are provided with an e-commerce application for sale of products and services responsive to the recommendations.

An intelligent Forcipomyia taiwana monitoring and management system comprises: a catching mechanism grabbing a to-be-identified target; a database storing a datum comprising pictures of a flying insect category; a model training module using the pictures to establish a training model; an image capture module shooting an image including the target; an identifying module selecting a first segmented region including the target by using YOLO detection framework technology, extracting a first identification feature from the target, and inputting the feature into the training model for deep learning to identify a flying insect category to which the target belongs and produce an identification result; a counting module recording a number of the target into the database; and a predictive tracking module obtaining a marked object based on the result marked with the target identified in the image, and using a Monte-Carlo category algorithm to track and predict the object.

An intelligent Forcipomyia taiwana monitoring and management system comprises: a catching mechanism grabbing a to-be-identified target; a database storing a datum comprising pictures of a flying insect category; a model training module using the pictures to establish a training model; an image capture module shooting an image including the target; an identifying module selecting a first segmented region including the target by using YOLO detection framework technology, extracting a first identification feature from the target, and inputting the feature into the training model for deep learning to identify a flying insect category to which the target belongs and produce an identification result; a counting module recording a number of the target into the database; and a predictive tracking module obtaining a marked object based on the result marked with the target identified in the image, and using a Monte-Carlo category algorithm to track and predict the object.

The present application provides a negative pressure boosting net cover and an electric fan mosquito swatter. The negative pressure boosting net cover comprises a bracket and a negative pressure boosting assembly, wherein the bracket is connected to the negative pressure boosting assembly, and the negative pressure boosting assembly is fixed at the air inlet of the fan cover shell. The negative pressure boosting assembly is composed of at least one negative pressure boosting body, which are fixed at the air inlet of the fan cover shell through brackets. Gaps between the negative pressure boosting body and the negative pressure boosting body/fan cover shell forms the ventilation openings and air passages. The technical solution of the present application expands the mosquito trapping range, enhances the suction force, and improves the mosquito trapping efficiency; on the premise that the limited power of the fan is not increased.

An inflatable flying insect trap that serves also serves as a light source. The insect trap is formed of an inflatable globe that, once inflated, forms a curved wall upon which a glue trap may be mounted. One or more LEDs positioned within the cavity of the globe emits light that passes through the globe and through the attached glue trap. Insects attracted to the light will become adhered to the external surface of the glue trap.

An inflatable flying insect trap that serves also serves as a light source. The insect trap is formed of an inflatable globe that, once inflated, forms a curved wall upon which a glue trap may be mounted. One or more LEDs positioned within the cavity of the globe emits light that passes through the globe and through the attached glue trap. Insects attracted to the light will become adhered to the external surface of the glue trap.

A base assembly has an upward plate, a downward plate, a side wall, a front, a back, and laterally spaced sides. A drive assembly has drive wheels extending downwardly from the downward plate, a caster wheel, and proximity sensors. A vacuum assembly has a vacuum slit in the downward plate between the lateral sides and a vacuum-generating source capable of pulling a vacuum through the vacuum slit. A tower assembly is adapted to attract and kill insects while the base assembly and the tower assembly are being optionally driven by the drive assembly and the vacuum assembly is vacuuming up insects.