Application of deep learning models for pest detection and identification DOI Creative Commons

Ayesha Rafique,

Madiha Abbasi,

Noreen Akram

et al.

Mehran University Research Journal of Engineering and Technology, Journal Year: 2025, Volume and Issue: 44(2), P. 117 - 128

Published: April 9, 2025

The quality and productivity of crops are seriously threatened by insect infestations, which is the primary focus this research. Traditional monitoring methodologies tend to be ineffective incorrect, resulting in wasted resources loss money. By incorporating cutting-edge AI deep learning technologies, study unveils a fresh method for rapid precisely identifying pests agricultural settings. This research makes use high-resolution image technologies Convolutional Neural Networks (CNNs) showcase promise models automated pest detection. generalizability model performance may improved using transfer techniques leading more efficient available resources. Key goals include extensive testing across varied types environmental settings, combined with design refinement CNN specifically engineered accurate identification. gap between traditional practices data-driven procedures filled suggested ensures significant increase that will contribute greater food security overall economic prosperity. strengthens influential effects on agriculture, including enhancement control, increasing security, boosting expansion. To promote continuous cooperation academics, businesses, farmers essential.

Language: Английский

RTR_Lite_MobileNetV2: A Lightweight and Efficient Model for Plant Disease Detection and Classification DOI Creative Commons
Sangeeta Duhan, Preeti Gulia, Nasib Singh Gill

et al.

Current Plant Biology, Journal Year: 2025, Volume and Issue: unknown, P. 100459 - 100459

Published: Feb. 1, 2025

Language: Английский

Citations

0
Artificial Intelligence for Fostering Sustainable Agriculture DOI Creative Commons

Konathala Kusumavathi,

Ramesh Konatala,

Priyanka Lal

et al.

Current Plant Biology, Journal Year: 2025, Volume and Issue: unknown, P. 100476 - 100476

Published: March 1, 2025

Language: Английский

Citations

0
Application of deep learning models for pest detection and identification DOI Creative Commons

Ayesha Rafique,

Madiha Abbasi,

Noreen Akram

et al.

Mehran University Research Journal of Engineering and Technology, Journal Year: 2025, Volume and Issue: 44(2), P. 117 - 128

Published: April 9, 2025

The quality and productivity of crops are seriously threatened by insect infestations, which is the primary focus this research. Traditional monitoring methodologies tend to be ineffective incorrect, resulting in wasted resources loss money. By incorporating cutting-edge AI deep learning technologies, study unveils a fresh method for rapid precisely identifying pests agricultural settings. This research makes use high-resolution image technologies Convolutional Neural Networks (CNNs) showcase promise models automated pest detection. generalizability model performance may improved using transfer techniques leading more efficient available resources. Key goals include extensive testing across varied types environmental settings, combined with design refinement CNN specifically engineered accurate identification. gap between traditional practices data-driven procedures filled suggested ensures significant increase that will contribute greater food security overall economic prosperity. strengthens influential effects on agriculture, including enhancement control, increasing security, boosting expansion. To promote continuous cooperation academics, businesses, farmers essential.

Language: Английский

Citations

0