Smart Farming with Sooty Tern Optimization based LS-HGNet Classification Model

V. Gokula Krishnan; B. Vikranth; M. Sumithra; B. Prathusha Laxmi; B. Shyamala Gowri

doi:10.52756/ijerr.2024.v37spl.008

V. Gokula Krishnan Department of CSE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, Tamil Nadu, India https://orcid.org/0009-0005-6819-6729
B. Vikranth Department of CSE-Cyber Security, CVR College of Engineering, Hyderabad, Telangana, India https://orcid.org/0000-0002-1424-6584
M. Sumithra Department of IT, Panimalar Engineering College, Poonamallee, Chennai, Tamil Nadu, India https://orcid.org/0000-0003-4934-9062
B. Prathusha Laxmi Department of AI & DS, R.M.K. College of Engineering and Technology, Gummidipoondi, Tamil Nadu, India https://orcid.org/0000-0003-2248-5486
B. Shyamala Gowri Department of CSE, Easwari Engineering College, Ramapuram, Chennai, Tamil Nadu, India https://orcid.org/0000-0003-1256-1836

DOI: https://doi.org/10.52756/ijerr.2024.v37spl.008

Keywords: Maize tassel, Drones, Deep learning, Stacked hourglass network, Sooty tern optimization

Abstract

Smart farming technologies enable farmers to use resources like water, fertilizer and pesticides as efficiently as possible. This paper discusses how Unmanned Aerial Vehicle (UAV) pictures can be used to automatically detect and count tassels, thereby advancing the advancement of strategic maize planting. The real state of affairs in cornfields is complicated, though, and the current algorithms struggle to provide the speed and accuracy required for real-time detection. This research employed a sizable, excellent dataset of maize tassels to solve this problem. This paper suggests using the bottom-hat-top-hat preprocessing technique to address the lighting irregularities and noise in maize photos taken by drones. The Lightweight weight-stacked hourglass Network (LS-HGNet) model is suggested for classification. The hourglass network structure of LS-HGNet, which is mostly utilised as a backbone network, has allowed significant advancements in the discovery of maize tassels. In light of this, the current work suggests a lighter variant of the hourglass network that also enhances the accuracy of tassel detection in maize plants. The additional skip connections used in the new hourglass network architecture allow minimal changes to the number of network parameters while improving performance. Consequently, the suggested LS-HGNet classifier lowers the computational burden and increases the convolutional receptive field. The hyperparameter tuning process is then carried out using the Sooty Tern Optimisation Algorithm (STOA), which helps increase tassel detection accuracy. Numerous tests were conducted to verify that the suggested approach is more accurate at 98.7% and more efficient than the most advanced techniques currently in use.

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