|
|
|
|
|
Haiping Si, Mingchun Li, Weixia Li, Guipei Zhang, Ming Wang, Feitao Li and Yanling Li
Apples, as the fourth-largest globally produced fruit, play a crucial role in modern agriculture. However, accurately identifying apple diseases remains a significant challenge as failure in this regard leads to economic losses and poses threats to food ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Huishan Li, Lei Shi, Siwen Fang and Fei Yin
Aiming at the problem of accurately locating and identifying multi-scale and differently shaped apple leaf diseases from a complex background in natural scenes, this study proposed an apple leaf disease detection method based on an improved YOLOv5s model...
ver más
|
|
|
|
|
|
|
|
|
|
|
Chenchen Gu, Chunjiang Zhao, Wei Zou, Shuo Yang, Hanjie Dou and Changyuan Zhai
Orchard spraying can effectively control pests and diseases. Over-spraying commonly results in excessive pesticide residues on agricultural products and environmental pollution. To avoid these problems, variable spraying technology uses target canopy det...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xiaopeng Li and Shuqin Li
The complex backgrounds of crop disease images and the small contrast between the disease area and the background can easily cause confusion, which seriously affects the robustness and accuracy of apple disease- identification models. To solve the above ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Arunabha M. Roy and Jayabrata Bhaduri
In this paper, a deep learning enabled object detection model for multi-class plant disease has been proposed based on a state-of-the-art computer vision algorithm. While most existing models are limited to disease detection on a large scale, the current...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xiaofei Chao, Xiao Hu, Jingze Feng, Zhao Zhang, Meili Wang and Dongjian He
The fast and accurate identification of apple leaf diseases is beneficial for disease control and management of apple orchards. An improved network for apple leaf disease classification and a lightweight model for mobile terminal usage was designed in th...
ver más
|
|
|
|
|
|