Parametric Analysis on Crop/Weed Classification via Optimized Convolutional Neural Network

Panda, Bikramaditya; Mishra, Manoj Kumar; Mishra, Bhabani Shankar Prasad; Tiwari, Abhinandan Kumar

doi:10.1109/icicv50876.2021.9388462

Cited by 1 publication

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional image classification algorithms, such as support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbor (KNN), etc., rely on early data processing and image dimensionality reduction to improve the accuracy and efficiency of the model. With the rapid development of CNN, image classification models based on CNN are constantly updated and upgraded [57]. Commonly used image classification algorithms include ConNeXt, GoogleNet, AlexNet, ResNet, etc., all of which have performed well in this task.…”

Section: Image Classificationmentioning

confidence: 99%

Advancements of UAV and Deep Learning Technologies for Weed Management in Farmland

Zhang,

Yu,

Zhang

et al. 2024

Agronomy

View full text Add to dashboard Cite

With the continuous growth of the global population and the increasing demand for crop yield, enhancing crop productivity has emerged as a crucial research objective on a global scale. Weeds, being one of the primary abiotic factors impacting crop yield, contribute to approximately 13.2% of annual food loss. In recent years, Unmanned Aerial Vehicle (UAV) technology has developed rapidly and its maturity has led to widespread utilization in improving crop productivity and reducing management costs. Concurrently, deep learning technology has become a prominent tool in image recognition. Convolutional Neural Networks (CNNs) has achieved remarkable outcomes in various domains, including agriculture, such as weed detection, pest identification, plant/fruit counting, maturity grading, etc. This study provides an overview of the development of UAV platforms, the classification of UAV platforms and their advantages and disadvantages, as well as the types and characteristics of data collected by common vision sensors used in agriculture, and discusses the application of deep learning technology in weed detection. The manuscript presents current advancements in UAV technology and CNNs in weed management tasks while emphasizing the existing limitations and future trends in its development process to assist researchers working on applying deep learning techniques to weed management.

show abstract

Section: Image Classificationmentioning

confidence: 99%