Mapping 1-km soybean yield across China from 2001 to 2020 based on ensemble learning by Min Zhang, Xinlei Xu, Junji Ou, Zengguang Zhang, Fangzheng Chen, Lijie Shi, Bin Wang, Meiqin Zhang, Liang He, Xueliang Zhang, Yong Chen, Kelin Hu, Puyu Feng

“…This dataset was generated by applying ensemble learning algorithms and spatial decomposition to a comprehensive set of multi-source data, including climate variables, remote sensing imagery, soil properties, agricultural management practices, and official yield records. …”
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Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning. by M Isabel Ramos, Juan J Cubillas, Ruth M Córdoba, Lidia M Ortega

“…The aim of this study is to anticipate accurate crop yield data at an early stage of the cropping season. …”
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Combining machine learning with UAV derived multispectral aerial images for wheat yield prediction, in southern Brazil by Henrique dos Santos Felipetto, Erivelto Mercante, Octavio Viana, Adão Robson Elias, Giovani Benin, Lucas Scolari, Arthur Armadori, Diandra Ganascini Donato

“…This research aims to evaluate the performance of machine learning algorithms and multispectral aerial images in estimating wheat grain yield, contributing to the eradication of hunger and food security. …”
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Sweet pepper yield modeling via deep learning and selection of superior genotypes using GBLUP and MGIDI by Hamid Hatami Maleki, Reza Darvishzadeh, Nasrin Azad

“…Here, 29 accessions of sweet pepper were investigated in two separate randomized complete block design with three replications in the field condition. Fruit yield accompanied by 13 agro-morphological traits were recorded in two experiments. …”
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Optimizing tomato yield prediction using phenologically timed UAV-based spectral data and machine learning by Carolina Trentin, Yiannis Ampatzidis, Sotirios Tasioulas, Pavlos Tsouvaltzis

“…This study evaluated the performance of machine learning models in predicting tomato yield using weather data, spectral bands, and vegetation indices under varying nitrogen rates and bio-stimulant treatments to induce plant growth variability. …”
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A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing Data by Dailiang Peng, Enhui Cheng, Xuxiang Feng, Jinkang Hu, Zihang Lou, Hongchi Zhang, Bin Zhao, Yulong Lv, Hao Peng, Bing Zhang

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Application of the digital annealer unit in optimizing chemical reaction conditions for enhanced production yields by Shih-Cheng Li, Pei-Hua Wang, Jheng-Wei Su, Wei-Yin Chiang, Tzu-Lan Yeh, Alex Zhavoronkov, Shih-Hsien Huang, Yen-Chu Lin, Chia-Ho Ou, Chih-Yu Chen

“…In active learning and autonomous reaction condition design, our model shows improvement for reaction yield prediction by incorporating new data, meaning that it can potentially be used in iterative processes. …”
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Artificial-Intelligence-Assisted Investigation of Quality and Yield of Cumene Production by Asad Ayub, Muhammad Zulkefal, Hamza Sethi

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Enhancing corn yield prediction: Optimizing data quality or model complexity? by Yuting Zhou, Shengfang Ma, Huihui Zhang, Sathyanarayanan Aakur

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Forecast of Sugarcane Yield in Chongzuo, Guangxi—LSTM Model Based on Fusion of Trend Yield and Meteorological Yield by Pengcheng Ma, Na Zhang, Yunhai Yang, Zeping Wang, Guodong Li, Zhishan Fu

“…The predicted yields were used again as input variables into the LSTM deep learning network to fit the nonlinear relationship between the two yields. …”
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Transcriptomic exploration yields novel perspectives on the regulatory network underlying trichome initiation in Gossypium arboreum hypocotyl by Yuxing Xie, Luying Yang, Zewei Zhao, Mingquan Ding, Yuefen Cao, Xin Hu, Junkang Rong

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Machine Learning-Driven Identification of Key Environmental Factors Influencing Fiber Yield and Quality Traits in Upland Cotton by Mohamadou Souaibou, Haoliang Yan, Panhong Dai, Jingtao Pan, Yang Li, Yuzhen Shi, Wankui Gong, Haihong Shang, Juwu Gong, Youlu Yuan

“…Understanding the influence of environmental factors on cotton performance is crucial for enhancing yield and fiber quality in the context of climate change. …”
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Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits. by Mohsen Yoosefzadeh-Najafabadi, Dan Tulpan, Milad Eskandari

“…These data were used for predicting the total soybean seed yield using the Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Random Forest (RF), machine learning (ML) algorithms, individually and collectively through an ensemble method based on bagging strategy (E-B). …”
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Machine Learning-Enabled Prediction and Mechanistic Analysis of Compressive Yield Strength–Hardness Correlation in High-Entropy Alloys by Haiyu Wan, Baobin Xie, Hui Feng, Jia Li

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Comparative study on Functional Machine learning and Statistical Methods in Disease detection and Weed Removal for Enhanced Agricultural Yield by Sudha D., Menaga D.

“…Conferring to existing statistics, most agriculturalists are facing severe losses due to poor farming yield. Farming activities are challenged by various environmental factors that affect agricultural productivity to a greater extent. …”
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Context-Aware Deep Learning Model for Yield Prediction in Potato Using Time-Series UAS Multispectral Data by Suraj A. Yadav, Xin Zhang, Nuwan K. Wijewardane, Max Feldman, Ruijun Qin, Yanbo Huang, Sathishkumar Samiappan, Wyatt Young, Francisco G. Tapia

“…The study demonstrated the efficacy of integrating time-series uncrewed aerial system (UAS) multispectral imaging with data-driven deep learning methodologies to systematically and precisely predict field-scale crop yield throughout the growing seasons. …”
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Evaluating Remote Sensing Resolutions and Machine Learning Methods for Biomass Yield Prediction in Northern Great Plains Pastures by Srinivasagan N. Subhashree, C. Igathinathane, John Hendrickson, David Archer, Mark Liebig, Jonathan Halvorson, Scott Kronberg, David Toledo, Kevin Sedivec

“…The top-ranked features (52 tested) from recursive feature elimination (RFE) were short-wave infrared 2, normalized difference moisture index, and average turf soil temperature in the machine learning (ML) model developed. The random forest (RF) model produced the highest accuracy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.83</mn></mrow></semantics></math></inline-formula>) among others tested for biomass yield prediction. …”
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Integrating Hyperspectral, Thermal, and Ground Data with Machine Learning Algorithms Enhances the Prediction of Grapevine Yield and Berry Composition by Shaikh Yassir Yousouf Jewan, Deepak Gautam, Debbie Sparkes, Ajit Singh, Lawal Billa, Alessia Cogato, Erik Murchie, Vinay Pagay

Subjects: “…grapevine yield…”
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When field experiments yield unexpected results: lessons learned from measuring selection in White Sands lizards. by Kayla M Hardwick, Luke J Harmon, Scott D Hardwick, Erica Bree Rosenblum

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Prediction of Soybean Yield at the County Scale Based on Multi-Source Remote-Sensing Data and Deep Learning Models by Hongkun Fu, Jian Li, Jian Lu, Xinglei Lin, Junrui Kang, Wenlong Zou, Xiangyu Ning, Yue Sun

Subjects: “…yield prediction…”
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