A multiscale point-supervised network for counting maize tassels in the wild
Accurate counting of maize tassels is essential for monitoring crop growth and estimating
crop yield. Recently, deep-learning-based object detection methods have been used for this …
crop yield. Recently, deep-learning-based object detection methods have been used for this …
Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning
Recent advancements in deep learning have brought significant improvements to plant
disease recognition. However, achieving satisfactory performance often requires high …
disease recognition. However, achieving satisfactory performance often requires high …
Plant Disease Recognition Datasets in the Age of Deep Learning: Challenges and Opportunities
Plant disease recognition has witnessed a significant improvement with deep learning in
recent years. Although plant disease datasets are essential and many relevant datasets are …
recent years. Although plant disease datasets are essential and many relevant datasets are …
From one field to another—Unsupervised domain adaptation for semantic segmentation in agricultural robotics
In traditional arable crop fields, tractors treat the whole field uniformly applying large
quantities of herbicides and pesticides for weed control and plant protection. Autonomous …
quantities of herbicides and pesticides for weed control and plant protection. Autonomous …
Using transfer learning-based plant disease classification and detection for sustainable agriculture
Subsistence farmers and global food security depend on sufficient food production, which
aligns with the UN's “Zero Hunger,”“Climate Action,” and “Responsible Consumption and …
aligns with the UN's “Zero Hunger,”“Climate Action,” and “Responsible Consumption and …
Known and unknown class recognition on plant species and diseases
Recognizing plant species and disease is essential to practical applications, such as
keeping biodiversity and obtaining a desired crop yield. This study aims to extend the …
keeping biodiversity and obtaining a desired crop yield. This study aims to extend the …
Unsupervised Domain Adaptation for Weed Segmentation Using Greedy Pseudo-labelling
Automatic weed identification based on RGB images with convolutional neural networks
(CNN) is a new frontier of precision agriculture. However the CNN models expect a large …
(CNN) is a new frontier of precision agriculture. However the CNN models expect a large …
Investigation to answer three key questions concerning plant pest identification and development of a practical identification framework
R Wayama, Y Sasaki, S Kagiwada, N Iwasaki… - … and Electronics in …, 2024 - Elsevier
The development of practical and robust automated diagnostic systems for identifying plant
pests is crucial for efficient agricultural production. In this paper, we first investigate three key …
pests is crucial for efficient agricultural production. In this paper, we first investigate three key …
Local and global feature-aware dual-branch networks for plant disease recognition
J Lin, X Zhang, Y Qin, S Yang, X Wen, T Cernava… - Plant …, 2024 - spj.science.org
Accurate identification of plant diseases is important for ensuring the safety of agricultural
production. Convolutional neural networks (CNNs) and visual transformers (VTs) can extract …
production. Convolutional neural networks (CNNs) and visual transformers (VTs) can extract …
An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …