相关论文: weedNet: Dense Semantic Weed Classification Using …
We present a novel weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider…
In modern agriculture, usually weeds control consists in spraying herbicides all over the agricultural field. This practice involves significant waste and cost of herbicide for farmers and environmental pollution. One way to reduce the cost…
Unmanned aerial vehicles (UAV) are used in precision agriculture (PA) to enable aerial monitoring of farmlands. Intelligent methods are required to pinpoint weed infestations and make optimal choice of pesticide. UAV can fly a multispectral…
In this paper we use convolutional neural networks (CNNs) for weed detection in agricultural land. We specifically investigate the application of two CNN layer types, Conv2d and dilated Conv2d, for weed detection in crop fields. The…
Smart weeding systems to perform plant-specific operations can contribute to the sustainability of agriculture and the environment. Despite monumental advances in autonomous robotic technologies for precision weed management in recent…
Reducing the use of agrochemicals is an important component towards sustainable agriculture. Robots that can perform targeted weed control offer the potential to contribute to this goal, for example, through specialized weeding actions such…
Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has been extensively carried out based on images captured with a…
Uncontrolled growth of weeds can severely affect the crop yield and quality. Unrestricted use of herbicide for weed removal alters biodiversity and cause environmental pollution. Instead, identifying weed-infested regions can aid selective…
Crop and weed monitoring is an important challenge for agriculture and food production nowadays. Thanks to recent advances in data acquisition and computation technologies, agriculture is evolving to a more smart and precision farming to…
The task of weed detection is an essential element of precision agriculture since accurate species identification allows a farmer to selectively apply herbicides and fits into sustainable agriculture crop management. This paper proposes a…
Most weed species can adversely impact agricultural productivity by competing for nutrients required by high-value crops. Manual weeding is not practical for large cropping areas. Many studies have been undertaken to develop automatic weed…
The task of weed detection is an essential element of precision agriculture since accurate species identification allows a farmer to selectively apply herbicides and fits into sustainable agriculture crop management. This paper proposes a…
The growth of weeds poses a significant challenge to agricultural productivity, necessitating efficient and accurate weed detection and management strategies. The combination of multispectral imaging and drone technology has emerged as a…
Agriculture is vital for human survival and remains a major driver of several economies around the world; more so in underdeveloped and developing economies. With increasing demand for food and cash crops, due to a growing global population…
Plant breeding programs extensively monitor the evolution of seed kernels for seed certification, wherein lies the need to appropriately label the seed kernels by type and quality. However, the breeding environments are large where the…
Deep learning, particularly Convolutional Neural Networks (CNNs), has gained significant attention for its effectiveness in computer vision, especially in agricultural tasks. Recent advancements in instance segmentation have improved image…
Modern agriculture heavily relies on Site-Specific Farm Management practices, necessitating accurate detection, localization, and quantification of crops and weeds in the field, which can be achieved using deep learning techniques. In this…
This paper presents SWNet, a bimodal end-to-end cross-spectral network specifically engineered for the detection of camouflaged weeds in dense agricultural environments. Plant camouflage, characterized by homochromatic blending where…
Machine learning has become a major field of research in order to handle more and more complex image detection problems. Among the existing state-of-the-art CNN models, in this paper a region-based, fully convolutional network, for fast and…
Weed management remains a critical challenge in agriculture, where weeds compete with crops for essential resources, leading to significant yield losses. Accurate detection of weeds at various growth stages is crucial for effective…