Classification and detection of insects from field images using deep learning for smart pest management: A systematic review

W Li, T Zheng, Z Yang, M Li, C Sun, X Yang - Ecological Informatics, 2021 - Elsevier
Insect pest is one of the main causes affecting agricultural crop yield and quality all over the
world. Rapid and reliable insect pest monitoring plays a crucial role in population prediction …

Deep learning for precision agriculture: A bibliometric analysis

S Coulibaly, B Kamsu-Foguem, D Kamissoko… - Intelligent Systems with …, 2022 - Elsevier
Recent advances in communication technologies with the emergence of connected objects
have changed the agricultural area. In this new digital age, the development of artificial …

Convolutional neural networks in computer vision for grain crop phenotyping: A review

YH Wang, WH Su - Agronomy, 2022 - mdpi.com
Computer vision (CV) combined with a deep convolutional neural network (CNN) has
emerged as a reliable analytical method to effectively characterize and quantify high …

A systematic review on automatic insect detection using deep learning

AC Teixeira, J Ribeiro, R Morais, JJ Sousa, A Cunha - Agriculture, 2023 - mdpi.com
Globally, insect pests are the primary reason for reduced crop yield and quality. Although
pesticides are commonly used to control and eliminate these pests, they can have adverse …

Recognizing wheat aphid disease using a novel parallel real-time technique based on mask scoring RCNN

V Kukreja, D Kumar, A Bansal… - 2022 2nd international …, 2022 - ieeexplore.ieee.org
Wheat is one of the most common cereal crops in India. Aphids cause extensive damage to
the whole wheat plant and lead to high yields loss. The aphid is transmitted on the summer …

Smart farming becomes even smarter with deep learning—a bibliographical analysis

Z Ünal - IEEE access, 2020 - ieeexplore.ieee.org
Smart farming is a new concept that makes agriculture more efficient and effective by using
advanced information technologies. The latest advancements in connectivity, automation …

Faster-PestNet: A Lightweight deep learning framework for crop pest detection and classification

F Ali, H Qayyum, MJ Iqbal - IEEE Access, 2023 - ieeexplore.ieee.org
One of the most significant risks impacting crops is pests, which substantially decrease food
production. Further, prompt and precise recognition of pests can help harvesters save …

DFF-ResNet: An insect pest recognition model based on residual networks

W Liu, G Wu, F Ren, X Kang - Big Data Mining and Analytics, 2020 - ieeexplore.ieee.org
Insect pest control is considered as a significant factor in the yield of commercial crops.
Thus, to avoid economic losses, we need a valid method for insect pest recognition. In this …

农业害虫检测的深度学习算法综述.

蒋心璐, 陈天恩, 王聪, 李书琴… - Journal of Computer …, 2023 - search.ebscohost.com
害虫检测是害虫测报的关键步骤, 对于害虫防治具有重要意义, 也是保证农作物产量和品质的
前提. 近年来, 随着卷积神经网络的迅速发展, 害虫检测技术进入智能化时代 …

Custom CornerNet: a drone-based improved deep learning technique for large-scale multiclass pest localization and classification

W Albattah, M Masood, A Javed, M Nawaz… - Complex & Intelligent …, 2023 - Springer
Insect pests are among the most critical factors affecting crops and result in a severe
reduction in food yield. At the same time, early and accurate identification of insect pests can …