AI and IoT-based collaborative business ecosystem: a case in Chinese fish farming industry

Despite living in a rural country, farmers in India face several challenges. Every year, they suffer significant losses due to agricultural insect infestation. These losses are primarily the result of inadequate field surveillance, crop diseases, and ineffective pesticide management. We need cutting-edge technology that is constantly evolving to maintain control over such major concerns responsible for output reductions year after year. Wireless sensor networks address all of these issues; in fact, wireless sensor network technology is quickly becoming the backbone of modern precision agriculture. We propose a strategy for pest monitoring using wireless sensor networks in this study by simply recognizing insect behaviour using various sensors. We proposed a rapid and accurate insect detection and categorization approach based on five important crops and associated insect pests. This method examines insect behaviour by collecting data from sensors placed in the field. The results show that the proposed work improves the accuracy of the existing work by 3.9 percent.

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An Artificial Neural Network-Based Pest Identification and Control in Smart Agriculture Using Wireless Sensor Networks

Singh

Kumar

Raja

et al. 2022

Journal of Food Quality

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AI and IoT-based collaborative business ecosystem: a case in Chinese fish farming industry

Abstract: This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Cited by 1 publication

References 29 publications

An Artificial Neural Network-Based Pest Identification and Control in Smart Agriculture Using Wireless Sensor Networks

An Artificial Neural Network-Based Pest Identification and Control in Smart Agriculture Using Wireless Sensor Networks

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