Enhancing Crop Yield Prediction Using IoT-Based Soil Moisture and Nutrient Sensors by Alsalami Zaid, Mohammed G., Srinivas Tummala

“…Crop yield prediction is crucial for ensuring food security by enabling farmers to optimize resource use, manage risks, and plan for market demands, ultimately leading to increased agricultural productivity and sustainability..The IoT-based crop yield prediction system integrates advanced sensing technologies, communication protocols, machine learning algorithms, and real-time monitoring to optimize crop production. …”
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A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data by Konstantinos Ntouros, Konstantinos Papatheodorou, Georgios Gkologkinas, Vasileios Drimzakas-Papadopoulos

“…The methodology introduces the “Area Under the Curve” (AUC) of vegetation indices as an independent variable for yield forecasting. Among the indices examined (NDVI, EVI, GNDVI, LAI, and a newly developed RE-PAP), GNDVI and LAI emerged as the most reliable predictors of wheat yield. …”
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Monitoring Yield and Quality of Forages and Grassland in the View of Precision Agriculture Applications—A Review by Abid Ali, Hans-Peter Kaul

“…At a larger scale, we discuss coupling of remote sensing with weather data (synergistic grassland yield modelling), Sentinel-2 data with radiative transfer modelling (RTM), Sentinel-1 backscatter, and Catboost–machine learning methods for digital mapping in terms of precision harvesting and site-specific farming decisions. …”
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Sensor-Based Bermudagrass Yield Prediction Models Using Random Forest Algorithm in Oklahoma by Gabriel Camargo de Campos Jezus, Lucas Freires Abreu, Daryl Brian Arnall, Lucas Martins Stolerman, Alexandre Caldeira Rocateli

“…Current literature states that (i) machine learning algorithms are promising in agriculture, and (ii) proximity and multispectral sensors can be employed to predict biomass. …”
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Cross-Year Rapeseed Yield Prediction for Harvesting Management Using UAV-Based Imagery by Yanni Zhang, Yaxiao Niu, Zhihong Cui, Xiaoyu Chai, Lizhang Xu

“…Machine learning (ML) models driven by remote sensing data are widely used for yield prediction. …”
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Kolmogorov–Arnold Networks for Interpretable Crop Yield Prediction Across the U.S. Corn Belt by Mustafa Serkan Isik, Ozan Ozturk, Mehmet Furkan Celik

“…We create a multi-source dataset that includes biophysical parameters along the crop phenology, as well as meteorological, topographic, and soil parameters to perform end-of-season and in-season predictions of county-level corn yields between 2016–2023. The performance of the KAN model is compared with the commonly used traditional machine learning (ML) models and its architecture-wise equivalent MLP. …”
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Enhancing paddy leaf disease diagnosis -a hybrid CNN model using simulated thermal imaging by Jagamohan Padhi, Kunal Mishra, Ashoka Kumar Ratha, Santi Kumari Behera, Prabira Kumar Sethy, Aziz Nanthaamornphong

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An Integrated Hybrid-Stochastic Framework for Agro-Meteorological Prediction Under Environmental Uncertainty by Mohsen Pourmohammad Shahvar, Davide Valenti, Alfonso Collura, Salvatore Micciche, Vittorio Farina, Giovanni Marsella

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Field-grown tomato yield estimation using point cloud segmentation with 3D shaping and RGB pictures from a field robot and digital single lens reflex cameras by B. Ambrus, G. Teschner, A.J. Kovács, M. Neményi, L. Helyes, Z. Pék, S. Takács, T. Alahmad, A. Nyéki

Subjects: “…Tomato yield estimation…”
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A crop model based on dual attention mechanism for large area adaptive yield prediction by Wei Xiang, Long Long, Zichen Liu, Feng Dai, Yucheng Zhang, Hu Li, Lin Cheng

“…With the rapid advancement of machine learning, methods for agricultural yield prediction based on deep learning models have become a primary means of improving prediction accuracy. …”
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Robust biochar yield and composition prediction via uncertainty-aware ResNet-based autoencoder by Yali Zhang, Bowen Lei, Amirhossein Mahdaviarab, Xiao Wang, Zong Liu

“…However, there is a crucial need for optimizing production conditions and minimizing environmental risks associated with biochar production. Machine learning is an emerging strategy for predicting biochar yield and composition, optimizing production conditions and minimizing environmental risks. …”
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Image-based yield prediction for tall fescue using random forests and convolutional neural networks by Sarah Ghysels, Bernard De Baets, Dirk Reheul, Steven Maenhout

“…A widely adopted strategy uses drone images processed by machine learning algorithms to characterise phenotypes. This approach was used in the present study to assess the dry matter yield of tall fescue and its accuracy was compared to that of the breeder’s evaluations, using field measurements as ground truth. …”
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The big question: why do change initiatives in education often fail to yield desired results? by Hope Nudzor

“…Although a wealth of fascinating literature on change management abounds in education, there is credible evidence to suggest that a lot of initiatives introduced with the view to bringing about improvement in pupils’ learning outcomes often fail to yield intended results. …”
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Data-driven modeling of the Yld2000 yield criterion and its efficient application in numerical simulation by Xiaomin Zhang, Jianzhong Mao, Zhi Cheng

“…Regression models for the yield stress and its first-order derivatives based on the Yld2000–2d yield criterion are developed using several machine learning algorithms, including Random Forest (RF), Multilayer Perceptron (MLP), Histogram-Based Gradient Boosting (HGB), and Support Vector Machine (SVM). …”
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Preliminary Investigation on Crop Growth, Physiology, and Yield of Rice under Partial Root-Zone Irrigation by Theivasigamani Parthasarathi, A. R. Nirmal kumar, Koothan Vanitha

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Article RETRACTED due to manipulation by the authors The Hybrid Learning System With Project Based Learning: Can It Increase Creative Thinking Skill and Learning Motivation in Phys... by Rifqi Festiawan, Eko Sumanto, Ayu Rizky Febriani, Asep Angga Permadi, Z. Arifin, Andri Wahyu Utomo, Wildan Alfia Nugroho, Kukuh Wahyudin Pratama

“…It can be concluded that 1) PjBL model has a favorable impact on students' critical thinking skills in physical education, 2) PjBL model positively influences learning motivation in physical education, and 3) PjBL model yields a more positive outcome compared to the direct teaching model in enhancing students' creative thinking skills in the context of physical education. …”
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Lessons learned and yet to be learned from predictors of youth crime research by Brianne M. Posey, Anastasiia Timmer, Nayan G. Ramirez

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Early monitoring of drought stress in safflower (Carthamus tinctorius L.) using hyperspectral imaging: A comparison of machine learning tools and feature selection approaches by Fatemeh Salek, Seyed Ahmad Mireei, Abbas Hemmat, Mehrnoosh Jafari, Mohammad R. Sabzalian, Majid Nazeri, Wouter Saeys

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Investigating The Effectiveness of Puzzle Teaching in Learning Disorders of Students with Learning Disabilities by Mahla Shabani, Zahra Usefvand, Mitra Tahanan, Zeinab Akhgar Geneh, Zeinab Abdimoghadam

“…Learning disorders constitute one of the primary contributing factors to students' academic underachievement. …”
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Enhanced wheat yield prediction through integrated climate and satellite data using advanced AI techniques by Muhamad Ashfaq, Imran Khan, Rana Fezan Afzal, Dilawar Shah, Shujaat Ali, Muhammad Tahir

“…Abstract Wheat plays a vital role in Pakistan’s economy and food security, making accurate yield forecasting essential for planning and resource management. …”
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