The objective of AI in agriculture is to control crop pests/diseases, reduce cost, and improve crop yield. In developing countries, the agriculture sector faces numerous challenges in the form of knowledge gap between farmers and technology, disease and pest infestation, lack of storage facilities,...
Finally, we propose the YOLO-Dense Pest (YOLO-DP) model for high-precision object detection of weed, pest, and disease crop images. We re-evaluate the state-of-the-art detection models with our proposed PDT dataset and CWC dataset, showing the completeness of the dataset and the ...
location (villages or cities), latitude, longitude of experiment site, soil type and pH at experimental sites, number of replicates, crop types, the initial year of NT practice, crop planting/harvesting month/year, and the period since the initial year of NT practice. ...
11:40 – 11:50Oral 4: Cross-Regional Oil Palm Tree Detection(Talk videohere) 11:50 – 11:00Oral 5: Effective Data Fusion with Generalized Vegetation Index(Talk videohere) 11:00 – 12:10Oral 6: Weakly Supervised Learning Guided by Activation Mapping Applied to a Novel Citrus Pest Benchmar...
As one of the important ways to change the appearance of face image, makeup transfer has received more and more attention in recent years. Makeup transfer networks can translate the makeup style of a reference image to any other non-makeup one while preserving face identity, helping people ...
Finally, we propose the YOLO-Dense Pest (YOLO-DP) model for high-precision object detection of weed, pest, and disease crop images. We re-evaluate the state-of-the-art detection models with our proposed PDT dataset and CWC dataset, showing the completeness of the dataset and the ...
The Agricultural Pest and Disease Image Recognition Dataset in Nanjing, Jiangsu Province, in 2023doi:10.19788/j.issn.2096-6369.230214Agricultural pests and diseases pose a serious threat to crop yield and quality, making accurate and efficient detection and identification ...
such as visual crop categorization [5], real-time plant disease and pest recognition [6], picking and harvesting automatic robots [7], healthy and quality monitoring of crop growing [8]. Moreover, there is increasing agricultural success present in the near future because deep-learning systems ...
For cropland, trends of millet yield per unit and average NDVI of cropland indicated high consistency with a linear regression determination coefficient of 0.94 (p < 0.01). Compared with other multi-target change detection methods, the changes detected by the MTHD could be related closely with ...
Machine vision, as a rapid and non-destructive detection method [5], has a wide range of applications in agriculture, including crop seed screening, crop pest and disease monitoring, grain detection, and plant phenotyping. In recent years, its rapid development in agriculture has recently made ...