Abstract:
The study presented in this abstract is about forecasting pest infestation in a field using an intelligent pest control system combining predictive modeling and real-time monitoring. The system aims to provide farmers with an efficient and proactive approach to managing pest infestations, thereby minimizing crop damage and increasing overall productivity. The proposed system incorporates smart traps equipped with various sensors and IoT connectivity. These traps can be strategically placed throughout the crop fields to monitor and capture pests effectively. The smart traps continuously collect data on pest population dynamics, including species identification, population density, movement patterns, temperature, humidity and timestamp. The collected data from the smart traps are utilized to create a historical database, providing valuable insights into the pest infestation trends specific to the crop and region. This historical data is then integrated into a predictive modeling framework, which employs machine learning algorithms and statistical techniques to forecast future pest outbreaks. The predictive modeling component utilizes a range of relevant parameters, such as weather conditions, crop growth stage, and historical pest data, to generate accurate predictions of potential pest infestations. By analyzing these forecasts, farmers can take proactive measures to prevent or control the infestation before significant damage occurs. The intelligent pest control system also incorporates real-time alerts and notifications to inform farmers about critical pest thresholds or sudden population spikes. This enables timely interventions, such as targeted pesticide application or deployment of natural pest control methods, resulting in precise and sustainable pest management practices. By integrating predictive modeling, historical data analysis, and smart trap monitoring, the proposed system offers an innovative approach to pest control in agriculture. It empowers farmers with advanced decision-making capabilities, allowing them to optimize pest management strategies, reduce reliance on chemical pesticides, and mitigate economic losses caused by pest damage.
Page(s):
83-83
DOI:
DOI not available
Published:
Journal: Abstract Book on International Conference on Food and Applied Sciences (ICFAS-23) 3-5 August 23, Volume: 0, Issue: 0, Year: 2023
Keywords:
pest management
,
control
,
Precision Agriculture
,
Prediction
,
smart farming