Challenges and opportunities in remote sensing-based crop monitoring: A review

B Wu, M Zhang, H Zeng, F Tian… - National Science …, 2023 - academic.oup.com
Building a more resilient food system for sustainable development and reducing uncertainty
in global food markets both require concurrent and near-real-time and reliable crop …

[HTML][HTML] Technologies for forecasting tree fruit load and harvest timing—from ground, sky and time

NT Anderson, KB Walsh, D Wulfsohn - Agronomy, 2021 - mdpi.com
The management and marketing of fruit requires data on expected numbers, size, quality
and timing. Current practice estimates orchard fruit load based on the qualitative …

[HTML][HTML] Machine learning for large-scale crop yield forecasting

D Paudel, H Boogaard, A de Wit, S Janssen… - Agricultural …, 2021 - Elsevier
Many studies have applied machine learning to crop yield prediction with a focus on specific
case studies. The data and methods they used may not be transferable to other crops and …

Extreme Gradient Boosting for yield estimation compared with Deep Learning approaches

F Huber, A Yushchenko, B Stratmann… - … and Electronics in …, 2022 - Elsevier
Accurate prediction of crop yield before harvest is of great importance for crop logistics,
market planning, and food distribution around the world. Yield prediction requires monitoring …

[HTML][HTML] Bridging the gap between crop breeding and GeoAI: Soybean yield prediction from multispectral UAV images with transfer learning

J Skobalski, V Sagan, H Alifu, O Al Akkad… - ISPRS Journal of …, 2024 - Elsevier
Despite significant progress has been made towards crop yield prediction with remote
sensing, there exist knowledge gaps on (1) the impacts of temporal resolution of imaging …

[HTML][HTML] Combining spectral and textural information in UAV hyperspectral images to estimate rice grain yield

F Wang, Q Yi, J Hu, L ** lands in the broadacre crop** region of Australia
Z **e, Y Zhao, R Jiang, M Zhang, G Hammer… - Remote Sensing of …, 2024 - Elsevier
Fallowing is an important strategy for enhancing soil health, water harvesting and crop
yields, thus improving sustainability and reducing production risks in dryland farming …

[HTML][HTML] Machine learning for regional crop yield forecasting in Europe

D Paudel, H Boogaard, A de Wit, M van der Velde… - Field Crops …, 2022 - Elsevier
Crop yield forecasting at national level relies on predictors aggregated from smaller spatial
units to larger ones according to harvested crop areas. Such crop areas come from land …

[HTML][HTML] Selection of independent variables for crop yield prediction using artificial neural network models with remote sensing data

P Hara, M Piekutowska, G Niedbała - Land, 2021 - mdpi.com
Knowing the expected crop yield in the current growing season provides valuable
information for farmers, policy makers, and food processing plants. One of the main benefits …

[HTML][HTML] Comparison of multi-source satellite images for classifying marsh vegetation using DeepLabV3 Plus deep learning algorithm

M Liu, B Fu, S **e, H He, F Lan, Y Li, P Lou, D Fan - Ecological Indicators, 2021 - Elsevier
The accurate classification of wetland vegetation is essential for rapid assessment and
management. The Honghe National Nature Reserve (HNNR), located in Northeast China …