[HTML][HTML] Machine learning in agriculture: A comprehensive updated review

L Benos, AC Tagarakis, G Dolias, R Berruto, D Kateris… - Sensors, 2021 - mdpi.com
The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …

[HTML][HTML] Review on convolutional neural network (CNN) applied to plant leaf disease classification

J Lu, L Tan, H Jiang - Agriculture, 2021 - mdpi.com
Crop production can be greatly reduced due to various diseases, which seriously endangers
food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional …

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 …

[HTML][HTML] Pre-trained deep neural network-based features selection supported machine learning for rice leaf disease classification

M Aggarwal, V Khullar, N Goyal, A Singh, A Tolba… - Agriculture, 2023 - mdpi.com
Rice is a staple food for roughly half of the world's population. Some farmers prefer rice
cultivation to other crops because rice can thrive in a wide range of environments. Several …

[HTML][HTML] 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 …

[HTML][HTML] YOLO-based detection of Halyomorpha halys in orchards using RGB cameras and drones

FB Sorbelli, L Palazzetti, CM Pinotti - Computers and electronics in …, 2023 - Elsevier
This paper explores the utilization of innovative technologies such as RGB cameras, drones,
and computer vision algorithms, for monitoring pests in orchards, with a specific focus on …

我国智能农机的研究进展与无人农场的实践

罗锡文, 廖娟, 胡炼, 周志艳, 张智刚, 臧英… - 华南农业大学 …, 2021 - journal.scau.edu.cn
智慧农业是现代农业的高级形式, 无人农场是实现智慧农业的重要途径, 智能农机是无人农场的
物质支撑. 本文以植物生产为例, 介绍了智能农机的智能感知, 自动导航, 精准作业和智慧管理4 …

[HTML][HTML] Metal surface defect detection using modified YOLO

Y Xu, K Zhang, L Wang - Algorithms, 2021 - mdpi.com
Aiming at the problems of inefficient detection caused by traditional manual inspection and
unclear features in metal surface defect detection, an improved metal surface defect …

Recognition and counting of typical apple pests based on deep learning

T Wang, L Zhao, B Li, X Liu, W Xu, J Li - Ecological Informatics, 2022 - Elsevier
The recognition and counting of apple pests sampled by different sex attractants are very
important and significant for pest control. Convolutional neural networks (CNNs) are …

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 …