Towards intelligent and integrated pest management through an AIoT‐based monitoring system

Abstract

BACKGROUND Main bottleneck in facilitating integrated pest management (IPM) is the unavailability of reliable and immediate crop damage data. Without sufficient insect pest and plant disease information, farm managers are unable to make proper decisions to prevent crop damage. This work aims to present how an integrated system was able to drive farm managers towards sustainable and data‐driven IPM. RESULTS A system called Intelligent and Integrated Pest and Disease Management (I2PDM) system was developed. Edge computing devices were developed to automatically detect and recognize major greenhouse insect pests such as thrips (Frankliniella intonsa, Thrips hawaiiensis, and Thrips tabaci), and whiteflies (Bemisia argentifolii and Trialeurodes vaporariorum), to name a few, and measure environmental conditions including temperature, humidity, and light intensity, and send data to a remote server. The system has been installed in greenhouses producing tomatoes and orchids for gathering long‐term spatiotemporal insect pest count and environmental data, for as long as 1368 days. The findings demonstrated that the proposed system supported the farm managers in performing IPM‐related tasks. Significant yearly reductions in insect pest count as high as 50.7% were observed on the farms. CONCLUSION It was concluded that novel and efficient strategies can be achieved by using an intelligent IPM system, opening IPM to potential benefits that cannot be easily realized with a traditional IPM program. This is the first work that reports the development of an intelligent strategic model for IPM based on actual automatically collected long‐term data. The work presented herein can help in encouraging farm managers, researchers, experts, and industries to work together in implementing sustainable and data‐driven IPM. © 2022 Society of Chemical Industry.

Artificial intelligence-based solutions for climate change: a review

“…The I 2 PDM system is composed of an intelligent integrated pest management wireless sensor network that collects images, pest numbers, and species through sensor nodes and stores them in a database for analysis, thus generating models that can be visually translated into numerical information (Rustia et al 2020). The technique was applied to a tomato field, and the pesticide dose was reduced from 235 to 204 L/time (16%), indicating that insecticide spraying effectively reduced pests (Rustia et al 2022).…”

Enhancing Crop Protection and Yield through Precision Agriculture and Integrated Pest Management: A Comprehensive Review

“…PA's data-driven insights into soil conditions, microclimates, and plant health can serve to inform IPM strategies, allowing for more targeted and effective pest control measures [59]. For instance, drone technology equipped with multispectral cameras can provide real-time data on pest infestations, enabling timely interventions that align with IPM principles [12].…”

“…Recent studies have demonstrated that the integration of these two approaches leads to more efficient resource utilization, lower pesticide use, and improved crop yield [10,11]. For instance, a study [12] found that utilizing drone technology for pest monitoring in an IPM framework resulted in a 30% reduction in pesticide use. Similarly, [13] observed that soil sensors used for real-time monitoring of soil moisture and nutrient levels could significantly enhance the efficiency of IPM strategies.…”

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“…There are two digital approaches in monitoring insect pests: automated and semiautomated. A fully automated approach entails the use of wireless sensor nodes, which are equipped with cameras that capture sticky paper trap images [10]. The sticky paper trap images are analyzed locally or through cloud computing.…”