[HTML][HTML] Drones in agriculture: A review and bibliometric analysis

A Rejeb, A Abdollahi, K Rejeb, H Treiblmaier - Computers and electronics …, 2022 - Elsevier
Abstract Drones, also called Unmanned Aerial Vehicles (UAV), have witnessed a
remarkable development in recent decades. In agriculture, they have changed farming …

Vision-based navigation and guidance for agricultural autonomous vehicles and robots: A review

Y Bai, B Zhang, N Xu, J Zhou, J Shi, Z Diao - Computers and Electronics in …, 2023 - Elsevier
Autonomous navigation of agricultural robots and vehicles in agricultural environments is a
prerequisite for the accomplishment of various tasks. However, precision navigation of …

Applications of deep learning in precision weed management: A review

N Rai, Y Zhang, BG Ram, L Schumacher… - … and Electronics in …, 2023 - Elsevier
Deep Learning (DL) has been described as one of the key subfields of Artificial Intelligence
(AI) that is transforming weed detection for site-specific weed management (SSWM). In the …

Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: a review of sensor technology, measurement procedures, and data correction workflows

H Aasen, E Honkavaara, A Lucieer, PJ Zarco-Tejada - Remote Sensing, 2018 - mdpi.com
In the last 10 years, development in robotics, computer vision, and sensor technology has
provided new spectral remote sensing tools to capture unprecedented ultra-high spatial and …

DeepWeeds: A multiclass weed species image dataset for deep learning

A Olsen, DA Konovalov, B Philippa, P Ridd, JC Wood… - Scientific reports, 2019 - nature.com
Robotic weed control has seen increased research of late with its potential for boosting
productivity in agriculture. Majority of works focus on develo** robotics for croplands …

UAV-based crop and weed classification for smart farming

P Lottes, R Khanna, J Pfeifer… - … on robotics and …, 2017 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) and other robots in smart farming applications offer the
potential to monitor farm land on a per-plant basis, which in turn can reduce the amount of …

Forecasting yield by integrating agrarian factors and machine learning models: A survey

D Elavarasan, DR Vincent, V Sharma… - … and electronics in …, 2018 - Elsevier
The advancement in science and technology has led to a substantial amount of data from
various fields of agriculture to be incremented in the public domain. Hence a desideratum …

weednet: Dense semantic weed classification using multispectral images and mav for smart farming

I Sa, Z Chen, M Popović, R Khanna… - IEEE robotics and …, 2017 - ieeexplore.ieee.org
Selective weed treatment is a critical step in autonomous crop management as related to
crop health and yield. However, a key challenge is reliable and accurate weed detection to …

Comparison of object detection and patch-based classification deep learning models on mid-to late-season weed detection in UAV imagery

AN Veeranampalayam Sivakumar, J Li, S Scott… - Remote Sensing, 2020 - mdpi.com
Mid-to late-season weeds that escape from the routine early-season weed management
threaten agricultural production by creating a large number of seeds for several future …

An automatic random forest-OBIA algorithm for early weed map** between and within crop rows using UAV imagery

AI De Castro, J Torres-Sánchez, JM Peña… - Remote Sensing, 2018 - mdpi.com
Accurate and timely detection of weeds between and within crop rows in the early growth
stage is considered one of the main challenges in site-specific weed management (SSWM) …