相关论文: RoWeeder: Unsupervised Weed Mapping through Crop-R…
In light of growing challenges in agriculture with ever growing food demand across the world, efficient crop management techniques are necessary to increase crop yield. Precision agriculture techniques allow the stakeholders to make…
Accurate crop row detection is often challenged by the varying field conditions present in real-world arable fields. Traditional colour based segmentation is unable to cater for all such variations. The lack of comprehensive datasets in…
Applying agrochemicals is the default procedure for conventional weed control in crop production, but has negative impacts on the environment. Robots have the potential to treat every plant in the field individually and thus can reduce the…
An experimental field cropped with sugar-beet with a wide spreading of weeds has been used to test vegetation identification from drone visible imagery. Expert masked and hue-filtered pictures have been used to train several Machine…
Monitoring crop fields to map features like weeds can be efficiently performed with unmanned aerial vehicles (UAVs) that can cover large areas in a short time due to their privileged perspective and motion speed. However, the need for…
Weed management represents a critical challenge in agriculture, significantly impacting crop yields and requiring substantial resources for control. Effective weed monitoring and analysis strategies are crucial for implementing sustainable…
Effective seed sowing in precision agriculture is hindered by challenges such as residue accumulation, low soil temperatures, and hair pinning (crop residue pushed in the trench by furrow opener), which obstruct optimal trench formation.…
Modern scientific and technological advances allow botanists to use computer vision-based approaches for plant identification tasks. These approaches have their own challenges. Leaf classification is a computer-vision task performed for the…
The development of precision agriculture has gradually introduced automation in the agricultural process to support and rationalize all the activities related to field management. In particular, service robotics plays a predominant role in…
Spot spraying represents an efficient and sustainable method for reducing the amount of pesticides, particularly herbicides, used in agricultural fields. To achieve this, it is of utmost importance to reliably differentiate between crops…
Precision agriculture system is an arising idea that refers to overseeing farms utilizing current information and communication technologies to improve the quantity and quality of yields while advancing the human work required. The…
Agriculture 3.0 and 4.0 have gradually introduced service robotics and automation into several agricultural processes, mostly improving crops quality and seasonal yield. Row-based crops are the perfect settings to test and deploy smart…
This paper addresses the critical need for efficient and accurate weed segmentation from drone video in precision agriculture. A quality-aware modular deep-learning framework is proposed that addresses common image degradation by analyzing…
Precise weed management is essential for sustaining crop productivity and ecological balance. Traditional herbicide applications face economic and environmental challenges, emphasizing the need for intelligent weed control systems powered…
Monitoring plant health is crucial for maintaining agricultural productivity and food safety. Disruptions in the plant's normal state, caused by diseases, often interfere with essential plant activities, and timely detection of these…
Developing robust models for precision vegetable weeding is currently constrained by the scarcity of large-scale, annotated weed-crop datasets. To address this limitation, this study proposes a foundational crop-weed detection model by…
Severe weather events can cause large financial losses to farmers. Detailed information on the location and severity of damage will assist farmers, insurance companies, and disaster response agencies in making wise post-damage decisions.…
Monitoring agricultural activities is important to ensure food security. Remote sensing plays a significant role for large-scale continuous monitoring of cultivation activities. Time series remote sensing data were used for the generation…
Deploying deep learning models in agriculture is difficult because edge devices have limited resources, but this work presents a compressed version of EcoWeedNet using structured channel pruning, quantization-aware training (QAT), and…
Early identification of drought stress in crops is vital for implementing effective mitigation measures and reducing yield loss. Non-invasive imaging techniques hold immense potential by capturing subtle physiological changes in plants…