18,381 research outputs found
Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs
Precision farming robots, which target to reduce the amount of herbicides
that need to be brought out in the fields, must have the ability to identify
crops and weeds in real time to trigger weeding actions. In this paper, we
address the problem of CNN-based semantic segmentation of crop fields
separating sugar beet plants, weeds, and background solely based on RGB data.
We propose a CNN that exploits existing vegetation indexes and provides a
classification in real time. Furthermore, it can be effectively re-trained to
so far unseen fields with a comparably small amount of training data. We
implemented and thoroughly evaluated our system on a real agricultural robot
operating in different fields in Germany and Switzerland. The results show that
our system generalizes well, can operate at around 20Hz, and is suitable for
online operation in the fields.Comment: Accepted for publication at IEEE International Conference on Robotics
and Automation 2018 (ICRA 2018
Adaptive detection of volunteer potato plants in sugar beet fields
Volunteer potato is an increasing problem in crop rotations where winter temperatures are often not cold enough to kill tubers leftover from harvest. Poor control, as a result of high labor demands, causes diseases like Phytophthora infestans to spread to neighboring fields. Therefore, automatic detection and removal of volunteer plants is required. In this research, an adaptive Bayesian classification method has been developed for classification of volunteer potato plants within a sugar beet crop. With use of ground truth images, the classification accuracy of the plants was determined. In the non-adaptive scheme, the classification accuracy was 84.6 and 34.9% for the constant and changing natural light conditions, respectively. In the adaptive scheme, the classification accuracy increased to 89.8 and 67.7% for the constant and changing natural light conditions, respectively. Crop row information was successfully used to train the adaptive classifier, without having to choose training data in advanc
AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming
The combination of aerial survey capabilities of Unmanned Aerial Vehicles
with targeted intervention abilities of agricultural Unmanned Ground Vehicles
can significantly improve the effectiveness of robotic systems applied to
precision agriculture. In this context, building and updating a common map of
the field is an essential but challenging task. The maps built using robots of
different types show differences in size, resolution and scale, the associated
geolocation data may be inaccurate and biased, while the repetitiveness of both
visual appearance and geometric structures found within agricultural contexts
render classical map merging techniques ineffective. In this paper we propose
AgriColMap, a novel map registration pipeline that leverages a grid-based
multimodal environment representation which includes a vegetation index map and
a Digital Surface Model. We cast the data association problem between maps
built from UAVs and UGVs as a multimodal, large displacement dense optical flow
estimation. The dominant, coherent flows, selected using a voting scheme, are
used as point-to-point correspondences to infer a preliminary non-rigid
alignment between the maps. A final refinement is then performed, by exploiting
only meaningful parts of the registered maps. We evaluate our system using real
world data for 3 fields with different crop species. The results show that our
method outperforms several state of the art map registration and matching
techniques by a large margin, and has a higher tolerance to large initial
misalignments. We release an implementation of the proposed approach along with
the acquired datasets with this paper.Comment: Published in IEEE Robotics and Automation Letters, 201
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
With the advent of agriculture 3.0 and 4.0, researchers are increasingly
focusing on the development of innovative smart farming and precision
agriculture technologies by introducing automation and robotics into the
agricultural processes. Autonomous agricultural field machines have been
gaining significant attention from farmers and industries to reduce costs,
human workload, and required resources. Nevertheless, achieving sufficient
autonomous navigation capabilities requires the simultaneous cooperation of
different processes; localization, mapping, and path planning are just some of
the steps that aim at providing to the machine the right set of skills to
operate in semi-structured and unstructured environments. In this context, this
study presents a low-cost local motion planner for autonomous navigation in
vineyards based only on an RGB-D camera, low range hardware, and a dual layer
control algorithm. The first algorithm exploits the disparity map and its depth
representation to generate a proportional control for the robotic platform.
Concurrently, a second back-up algorithm, based on representations learning and
resilient to illumination variations, can take control of the machine in case
of a momentaneous failure of the first block. Moreover, due to the double
nature of the system, after initial training of the deep learning model with an
initial dataset, the strict synergy between the two algorithms opens the
possibility of exploiting new automatically labeled data, coming from the
field, to extend the existing model knowledge. The machine learning algorithm
has been trained and tested, using transfer learning, with acquired images
during different field surveys in the North region of Italy and then optimized
for on-device inference with model pruning and quantization. Finally, the
overall system has been validated with a customized robot platform in the
relevant environment
Hortibot: Feasibility study of a plant nursing robot performing weeding operations – part IV
Based on the development of a robotic tool carrier (Hortibot) equipped with weeding tools, a feasibility study was carried out to evaluate the viability of this innovative technology. The feasibility was demonstrated through a targeted evaluation adapted to the obtainable knowledge on the system performance in horticulture.
A usage scenario was designed to set the implementation of the robotic system in a row crop of seeded bulb onions considering operational and functional constraints in organic crop, production. This usage scenario together with the technical specifications of the implemented system provided the basis for the feasibility analysis, including a comparison with a conventional weeding system. Preliminary results show that the automation of the weeding tasks within a row crop has the potential of significantly reducing the costs and still fulfill the operational requirements set forth.
The potential benefits in terms of operational capabilities and economic viability have been quantified. Profitability gains ranging from 20 to 50% are achievable through targeted applications. In general, the analyses demonstrate the operational and economic feasibility of using small automated vehicles and targeted tools in specialized production settings
The Digitalisation of African Agriculture Report 2018-2019
An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains
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