| no |
title |
author |
magazine |
year |
volume |
issue |
page(s) |
type |
| 1 |
Adoption of internet of things-enabled agricultural systems among Chinese agro-entreprises
|
Yang, Qing
|
|
|
25 |
5 |
p. 2477-2504
|
article
|
| 2 |
Assessing grapevine water status in a variably irrigated vineyard with NIR/SWIR hyperspectral imaging from UAV
|
Laroche-Pinel, E.
|
|
|
25 |
5 |
p. 2356-2374
|
article
|
| 3 |
Assessment of spray patterns and efficiency of an unmanned sprayer used in planar growing systems
|
Kang, Chenchen
|
|
|
25 |
5 |
p. 2271-2291
|
article
|
| 4 |
Crop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies
|
Chakhvashvili, Erekle
|
|
|
25 |
5 |
p. 2614-2642
|
article
|
| 5 |
Detection and localization of citrus picking points based on binocular vision
|
Hou, Chaojun
|
|
|
25 |
5 |
p. 2321-2355
|
article
|
| 6 |
Estimation of corn crop damage caused by wildlife in UAV images
|
Aszkowski, Przemysław
|
|
|
25 |
5 |
p. 2505-2530
|
article
|
| 7 |
Evaluating the utility of combining high resolution thermal, multispectral and 3D imagery from unmanned aerial vehicles to monitor water stress in vineyards
|
Burchard-Levine, V.
|
|
|
25 |
5 |
p. 2447-2476
|
article
|
| 8 |
Farmers’ willingness to adopt precision agricultural technologies to reduce mycotoxin contamination in grain: evidence from grain farmers in Spain and Lithuania
|
Owusu-Sekyere, Enoch
|
|
|
25 |
5 |
p. 2292-2320
|
article
|
| 9 |
Field validation of NDVI to identify crop phenological signatures
|
Bhatti, Muhammad Tousif
|
|
|
25 |
5 |
p. 2245-2270
|
article
|
| 10 |
From pen and paper to digital precision: a comprehensive review of on-farm recordkeeping
|
Basir, Md. Samiul
|
|
|
25 |
5 |
p. 2643-2682
|
article
|
| 11 |
Incorporation of mechanistic model outputs as features for data-driven models for yield prediction: a case study on wheat and chickpea
|
Al-Shammari, Dhahi
|
|
|
25 |
5 |
p. 2531-2553
|
article
|
| 12 |
Integrating NDVI and agronomic data to optimize the variable-rate nitrogen fertilization
|
Silvestri, Nicola
|
|
|
25 |
5 |
p. 2554-2572
|
article
|
| 13 |
Integrative approaches in modern agriculture: IoT, ML and AI for disease forecasting amidst climate change
|
Delfani, Payam
|
|
|
25 |
5 |
p. 2589-2613
|
article
|
| 14 |
Mechanized wet direct seeding for increased rice production efficiency and reduced carbon footprint
|
Van Hung, Nguyen
|
|
|
25 |
5 |
p. 2226-2244
|
article
|
| 15 |
On-farm cereal rye biomass estimation using machine learning on images from an unmanned aerial system
|
KC, Kushal
|
|
|
25 |
5 |
p. 2198-2225
|
article
|
| 16 |
On-farm evaluation of a crop forecast-based approach for season-specific nitrogen application in winter wheat
|
M., Palka
|
|
|
25 |
5 |
p. 2394-2420
|
article
|
| 17 |
Orbital multispectral imaging: a tool for discriminating management strategies for nematodes in coffee
|
Orlando, Vinicius Silva Werneck
|
|
|
25 |
5 |
p. 2573-2588
|
article
|
| 18 |
Remote sensing imagery to predict soybean yield: a case study of vegetation indices contribution
|
Amaral, Lucas R.
|
|
|
25 |
5 |
p. 2375-2393
|
article
|
| 19 |
Simulating within-field spatial and temporal corn yield response to nitrogen with APSIM model
|
Thompson, Laura J.
|
|
|
25 |
5 |
p. 2421-2446
|
article
|
| 20 |
Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues
|
Jennewein, Jyoti S.
|
|
|
25 |
5 |
p. 2165-2197
|
article
|
Journal description