Machine learning in agriculture: A comprehensive updated review

L Benos, AC Tagarakis, G Dolias, R Berruto, D Kateris… - Sensors, 2021 - mdpi.com
The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …

State of major vegetation indices in precision agriculture studies indexed in web of science: A review

D Radočaj, A Šiljeg, R Marinović, M Jurišić - Agriculture, 2023 - mdpi.com
Vegetation indices provide information for various precision-agriculture practices, by
providing quantitative data about crop growth and health. To provide a concise and up-to …

Can yield prediction be fully digitilized? A systematic review

N Darra, E Anastasiou, O Kriezi, E Lazarou, D Kalivas… - Agronomy, 2023 - mdpi.com
Going beyond previous work, this paper presents a systematic literature review that explores
the deployment of satellites, drones, and ground-based sensors for yield prediction in …

Phenological stage and vegetation index for predicting corn yield under rainfed environments

A Shrestha, R Bheemanahalli, A Adeli… - Frontiers in Plant …, 2023 - frontiersin.org
Uncrewed aerial systems (UASs) provide high temporal and spatial resolution information
for crop health monitoring and informed management decisions to improve yields. However …

Machine learning technology for early prediction of grain yield at the field scale: A systematic review

J Leukel, T Zimpel, C Stumpe - Computers and Electronics in Agriculture, 2023 - Elsevier
Abstract Machine learning (ML) has become an important technology for the development of
prediction models for crop yield. Predictive modeling using ML is rapidly growing as …

Estimating yield-related traits using UAV-derived multispectral images to improve rice grain yield prediction

MV Bascon, T Nakata, S Shibata, I Takata… - Agriculture, 2022 - mdpi.com
Rice grain yield prediction with UAV-driven multispectral images are re-emerging interests
in precision agriculture, and an optimal sensing time is an important factor. The aims of this …

[HTML][HTML] Evaluation of UAV multispectral cameras for yield and biomass prediction in wheat under different sun elevation angles and phenological stages

S Shafiee, T Mroz, I Burud, M Lillemo - Computers and Electronics in …, 2023 - Elsevier
Quantitative trait prediction using multispectral UAV imagery is gaining popularity in field
trials. However, the reliability of models is influenced by the type of camera and its …

Corn grain yield prediction using UAV-based high spatiotemporal resolution imagery, machine learning, and spatial cross-validation

P Killeen, I Kiringa, T Yeap, P Branco - Remote Sensing, 2024 - mdpi.com
Food demand is expected to rise significantly by 2050 due to the increase in population;
additionally, receding water levels, climate change, and a decrease in the amount of …

Nutritional status assessment of olive crops by means of the analysis and modelling of multispectral images taken with UAVs

M Noguera, A Aquino, JM Ponce, A Cordeiro… - Biosystems …, 2021 - Elsevier
Highlights•Method for NPK leaf content retrieval in olive trees by means of multispectral
data.•Multispectral images taken by a UAV under field conditions.•Image analysis algorithm …

Analysis of corn yield prediction potential at various growth phases using a process-based model and deep learning

Y Ren, Q Li, X Du, Y Zhang, H Wang, G Shi, M Wei - Plants, 2023 - mdpi.com
Early and accurate prediction of grain yield is of great significance for ensuring food security
and formulating food policy. The exploration of key growth phases and features is beneficial …