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] High-throughput phenotyping: Breaking through the bottleneck in future crop breeding

P Song, J Wang, X Guo, W Yang, C Zhao - The Crop Journal, 2021 - Elsevier
With the rapid development of genetic analysis techniques and crop population size,
phenotyping has become the bottleneck restricting crop breeding. Breaking through this …

[HTML][HTML] A new mobile application of agricultural pests recognition using deep learning in cloud computing system

ME Karar, F Alsunaydi, S Albusaymi… - Alexandria Engineering …, 2021 - Elsevier
Agricultural pests cause between 20 and 40 percent loss of global crop production every
year as reported by the Food and Agriculture Organization (FAO). Therefore, smart …

[PDF][PDF] Automatic detection and monitoring of insect pests—A review

MCF Lima, MED de Almeida Leandro, C Valero… - Agriculture, 2020 - researchgate.net
Many species of insect pests can be detected and monitored automatically. Several systems
have been designed in order to improve integrated pest management (IPM) in the context of …

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 …

Pest24: A large-scale very small object data set of agricultural pests for multi-target detection

QJ Wang, SY Zhang, SF Dong, GC Zhang… - … and Electronics in …, 2020 - Elsevier
Precision agriculture poses new challenges for real-time monitoring pest population in field
based on new-generation AI technology. In order to provide a big data resource for training …

Classification of plant leaf diseases based on improved convolutional neural network

J Hang, D Zhang, P Chen, J Zhang, B Wang - Sensors, 2019 - mdpi.com
Plant leaf diseases are closely related to people's daily life. Due to the wide variety of
diseases, it is not only time-consuming and labor-intensive to identify and classify diseases …

Apple leaf diseases recognition based on an improved convolutional neural network

Q Yan, B Yang, W Wang, B Wang, P Chen, J Zhang - Sensors, 2020 - mdpi.com
Scab, frogeye spot, and cedar rust are three common types of apple leaf diseases, and the
rapid diagnosis and accurate identification of them play an important role in the …

Apple-Net: A model based on improved YOLOv5 to detect the apple leaf diseases

R Zhu, H Zou, Z Li, R Ni - Plants, 2022 - mdpi.com
Effective identification of apple leaf diseases can reduce pesticide spraying and improve
apple fruit yield, which is significant to agriculture. However, the existing apple leaf disease …

Tomato anomalies detection in greenhouse scenarios based on YOLO-Dense

X Wang, J Liu - Frontiers in Plant Science, 2021 - frontiersin.org
Greenhouse cultivation can improve crop yield and quality, and it not only solves people's
daily needs but also brings considerable gains to the agricultural staff. One of the most …