Autonomous agricultural insect pest monitoring station

• Grant number: 19/19552-0
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
• Duration: September 01, 2020 - August 31, 2022
• Field of knowledge: Interdisciplinary Subjects
• Principal Investigator: Bárbara Mirelli de Melo e Castro
• Grantee: Bárbara Mirelli de Melo e Castro
• Host Company: AFH Soluções Tecnológicas Ltda
• CNAE: Consultoria em tecnologia da informação ; Pesquisa e desenvolvimento experimental em ciências físicas e naturais
• City: Campinas
• Pesquisadores principais: Andrei Grespan
• Associated research grant: 17/22531-9 - Autonomous monitoring station for agricultural insects-pests, AP.PIPE
• Associated scholarship(s): 21/03856-0 - Autonomous agricultural insect pest monitoring station, BP.TT; 20/15475-8 - Autonomous agricultural insect pest monitoring station, BP.TT; 20/13342-0 - Autonomous agricultural insect pest monitoring station, BP.TT; 20/13209-9 - Autonomous agricultural insect pest monitoring station, BP.PIPE

Abstract

The order Lepidoptera is the one that has the largest number of insect species considered agricultural pests, whose larval phase can damage all parts of cultivated plants, being responsible for damages in the order of billions of reais to Brazilian agriculture. Evaluations on all traditional methods of monitoring these pests are done manually, a process that requires mobilization of field personnel and can be time-consuming compared to insect population dynamics. The long time between evaluations can make it difficult to effectively monitor the level of infestation in the area, which is crucial for the implementation of pest control strategies. This research project was designed with the overall objective of advancing the development of the pest monitoring trap that uses image processing for pest counting and classification, initiated in the PIPE Phase 1 research project, process FAPESP No. 2017 / 22531-9. The technology will allow daily reading of pest population dynamics, integration of data into a WEB server and dedicated reports for more efficient and accurate pest management. As expected results, we intend to obtain the prototype product in its initial version, capable of operating in cotton, soybean, corn and sugarcane production units, transmitting the data to the WEB platform and enabling easy access and interpretation of the information. Project activities were divided by knowledge area. The teams responsible for each stage were selected to obtain the best performance. Field performance and operational tests will be conducted for the assessment of the progress of activities and for the preparation of project reports. Thus, the solution brings important innovations for Brazilian agriculture, digital agriculture and the economic gains of the main crops of the country. (AU)