Results 1 to 10 of about 289,913 (165)

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

J Chem Inf Model, 2023
Machine Learning (ML) 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

Early Apple Yield Prediction Based on Flowering Stage Image Thinning Simulation Characteristics [PDF]

Plants
The existing fruit tree yield prediction methods mainly rely on fruit period images or long-term meteorological and soil data, which make it difficult to meet the needs of early yield prediction.
Qihang Yang, Liqun Liu
doaj   +2 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

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

Yield Predict: A Crop Yield Prediction Framework for Smart Farms

International Journal of Advanced Research in Science, Communication and Technology, 2023
In today’s world the most important thing for living in the Indian economy is agriculture.In recent years, machine learning approaches are gaining popularity with the advent of big data. Adoption of these approaches in the agricultural sector has immense potential to increase crop productivity and quality.
null Shinde Shivnanda, null Pawar Nikita, null Dhage Tanaji   +2 more
openaire   +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

BASES FOR THE PREDICTION OF CORN YIELDS [PDF]

Plant Physiology, 1950
Accurate crop yield estimates at the earliest possible date are of major importance to producers, handlers, warehousers, railroads, and consumers, whose plans and operations depend upon the knowledge of present and future stocks of their commodity. Past attempts at crop prediction have been largely of two types. The Crop Reporting Board of the U.
R H, Shaw, W E, Loomis
openaire   +2 more sources