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When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges. [PDF]

J Chem Inf Model, 2023
Machine Learning techniques face significant challenges when predicting advanced chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction conditions. These challenges stem from the high-dimensional nature of the prediction task and the myriad essential variables involved, ranging from reactants and reagents to ...
Voinarovska V, Kabeshov M, Dudenko D, Genheden S, Tetko IV.   +4 more
europepmc   +4 more sources

Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US [PDF]

Sensors
Efficient and reliable corn (Zea mays L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers.
Sayantan Sarkar, Javier M. Osorio Leyton, Efrain Noa-Yarasca, Kabindra Adhikari, Chad B. Hajda, Douglas R. Smith   +5 more
doaj   +2 more sources

Maize yield prediction with trait-missing data via bipartite graph neural network [PDF]

Frontiers in Plant Science
The timely and accurate prediction of maize (Zea mays L.) yields prior to harvest is critical for food security and agricultural policy development.
Kaiyi Wang, Kaiyi Wang, Yanyun Han, Yanyun Han, Yuqing Zhang, Yong Zhang, Shufeng Wang, Shufeng Wang, Feng Yang, Feng Yang, Chunqing Liu, Dongfeng Zhang, Dongfeng Zhang, Tiangang Lu, Like Zhang, Zhongqiang Liu, Zhongqiang Liu   +16 more
doaj   +2 more sources

California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach [PDF]

Frontiers in Plant Science, 2019
California’s almond growers face challenges with nitrogen management as new legislatively mandated nitrogen management strategies for almond have been implemented.
Zhou Zhang, Zhou Zhang, Yufang Jin, Bin Chen, Patrick Brown   +4 more
doaj   +4 more sources

UAV-Based Yield Prediction Based on LAI Estimation in Winter Wheat (Triticum aestivum L.) Under Different Nitrogen Fertilizer Types and Rates [PDF]

Plants
The rapid and accurate prediction of crop yield and the construction of optimal yield prediction models are important for guiding field-scale agronomic management practices in precision agriculture. This study selected the leaf area index (LAI) of winter
Jinjin Guo, Xiangtong Zeng, Qichang Ma, Yong Yuan, Nv Zhang, Zhizhao Lin, Pengzhou Yin, Hanran Yang, Xiaogang Liu, Fucang Zhang   +9 more
doaj   +2 more sources

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments [PDF]

Frontiers in Plant Science
In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights.
Claudia Aviles Toledo, Melba M. Crawford, Melba M. Crawford, Mitchell R. Tuinstra   +3 more
doaj   +2 more sources

Wheat Yield Prediction Using Unmanned Aerial Vehicle RGB-Imagery-Based Convolutional Neural Network and Limited Training Samples

Remote Sensing, 2023
Low-cost UAV RGB imagery combined with deep learning models has demonstrated the potential for the development of a feasible tool for field-scale yield prediction.
Juncheng Ma, Yongfeng Wu, Binhui Liu, Wenying Zhang, Bianyin Wang, Zhaoyang Chen, Guangcai Wang, Anqiang Guo   +7 more
doaj   +1 more source

County-level corn yield prediction using supervised machine learning

European Journal of Remote Sensing, 2023
The main objectives of this study are (1) to compare several machine learning models to predict county-level corn yield in the study area and (2) to compare the feasibility of machine learning models for in-season yield prediction.
Shahid Nawaz Khan, Abid Nawaz Khan, Aqil Tariq, Linlin Lu, Naeem Abbas Malik, Muhammad Umair, Wesam Atef Hatamleh, Farah Hanna Zawaideh   +7 more
doaj   +1 more source

When SMILES Smiles, Practicality Judgment and Yield Prediction of Chemical Reaction via Deep Chemical Language Processing

IEEE Access, 2021
Simplified Molecular Input Line Entry System (SMILES) provides a text-based encoding method to describe the structure of chemical species and formulize general chemical reactions.
Shu Jiang, Zhuosheng Zhang, Hai Zhao, Jiangtong Li, Yang Yang, Bao-Liang Lu, Ning Xia   +6 more
doaj   +1 more source

Predicting Table Beet Root Yield with Multispectral UAS Imagery

Remote Sensing, 2021
Timely and accurate monitoring has the potential to streamline crop management, harvest planning, and processing in the growing table beet industry of New York state.
Robert Chancia, Jan van Aardt, Sarah Pethybridge, Daniel Cross, John Henderson   +4 more
doaj   +1 more source