Tomato Yield Estimation Using an Improved Lightweight YOLO11n Network and an Optimized Region Tracking-Counting Method by Aichen Wang, Yuanzhi Xu, Dong Hu, Liyuan Zhang, Ao Li, Qingzhen Zhu, Jizhan Liu

Subjects: “…deep learning…”
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Forecasting yield and market classes of Vidalia sweet onions: A UAV-based multispectral and texture data-driven approach by Marcelo Rodrigues Barbosa Júnior, Lucas de Azevedo Sales, Regimar Garcia dos Santos, Rônega Boa Sorte Vargas, Chris Tyson, Luan Pereira de Oliveira

Subjects: “…Machine learning…”
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Machine Learning Prediction of Foaming in Anaerobic Co-Digestion from Six Key Process Parameters by Sarah E. Daly, Ji-Qin Ni

Subjects: “…machine learning…”
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A data-driven machine learning approach toward an improved maize crop production by Tosin Comfort Olayinka, Adebayo Olusola Adetunmbi, Olayinka Olumide Obe, Emmanuel Onwuka Ibam, Akinola Samson Olayinka

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Optimizing Wheat Yields: AI and IoT Techniques for Continuous Agricultural Monitoring by Nandy Manish, Afzal Md

“…The techniques of machine learning is applied to the historical and present data and then the crop performance and potential yield is predicted under different conditions. …”
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A Hybrid Model for Soybean Yield Prediction Integrating Convolutional Neural Networks, Recurrent Neural Networks, and Graph Convolutional Networks by Vikram S. Ingole, Ujwala A. Kshirsagar, Vikash Singh, Manish Varun Yadav, Bipin Krishna, Roshan Kumar

Subjects: “…soybean yield…”
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Editorial: Harnessing machine learning to decode plant-microbiome dynamics for sustainable agriculture by Mohsen Yoosefzadeh Najafabadi, Eman M. Khalaf, Eman M. Khalaf, Mohamed Mysara, Ahmed M. El-Baz

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Impact of Phenological and Lighting Conditions on Early Detection of Grapevine Inflorescences and Bunches Using Deep Learning by Rubén Íñiguez, Carlos Poblete-Echeverría, Ignacio Barrio, Inés Hernández, Salvador Gutiérrez, Eduardo Martínez-Cámara, Javier Tardáguila

Subjects: “…yield prediction…”
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A new integrated neurosymbolic approach for crop-yield prediction using environmental data and satellite imagery at field scale by Khadija Meghraoui, Teeradaj Racharak, Kenza Ait El Kadi, Saloua Bensiali, Imane Sebari

Subjects: “…Crop-yield prediction…”
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A Stride Toward Wine Yield Estimation from Images: Metrological Validation of Grape Berry Number, Radius, and Volume Estimation by Bernardo Lanza, Davide Botturi, Alessandro Gnutti, Matteo Lancini, Cristina Nuzzi, Simone Pasinetti

“…This paper focuses on the metrological validation of a novel deep-learning model that robustly estimates both the number and the radii of grape berries in vineyards using color images, allowing the computation of the visible (and total) volume of grape clusters, which is necessary to reach the ultimate goal of estimating yield production. …”
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A Digital Twin Framework With Bayesian Optimization and Deep Learning for Semiconductor Process Control by Chin-Yi Lin, Tzu-Liang Tseng, Tsung-Han Tsai

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A comprehensive review on deep learning applications in advancing biodiesel feedstock selection and production processes by Olugbenga Akande, Jude A. Okolie, Richard Kimera, Chukwuma C. Ogbaga

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Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms by Nizom Farmonov, Khilola Amankulova, Jozsef Szatmari, Alireza Sharifi, Dariush Abbasi-Moghadam, Seyed Mahdi Mirhoseini Nejad, Laszlo Mucsi

“…Developments in space-based hyperspectral sensors, advanced remote sensing, and machine learning can help crop yield measurement, modelling, prediction, and crop monitoring for loss prevention and global food security. …”
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A case study on canola (Brassica napus L.) potential yield prediction using remote sensing imagery and advanced data analytics by Nitin Rai, Harsh Pathak, Maria Villamil Mahecha, Dennis R. Buckmaster, Yanbo Huang, Paul Overby, Xin Sun

“…Additionally, to perform a comparative test with neural network-based deep learning (DL) architectures, a convolutional neural network-1-dimensional (CNN-1D_Canola) model was developed to train data points and predict canola yield. …”
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Phenological piecewise modelling is more conducive than whole-season modelling to winter wheat yield estimation based on remote sensing data by Xin Huang, Wenquan Zhu, Cenliang Zhao, Zhiying Xie, Hui Zhang

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APPLICATION OF REMOTE SENSING IN THE ESTIMATION OF AGRICULTURAL CROPS YIELDS: A BIBLIOMETRIC REVIEW by Irene Gutierrez Mora, Aleida Selene Hernández Cázares, Juan Valente Hidalgo Contreras, Jose Luis López Ayala, Joel Velasco Velasco

“…Objective: To analyze research on yield estimation/prediction in agricultural crops through satellite remote sensing in agricultural sciences. …”
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Data-driven machine learning with lattice distortion and thermodynamic parameters guided strength optimization of refractory high-entropy alloys by Shujian Ding, Yifan Zhang, Siyang Lei, Xiang Weng, Wenhui Li, Wei Ren, Jian Chen, Weili Wang

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Integrating Machine Learning for Enhanced Agricultural Productivity: A Focus on Bananas and Arecanut in the Context of India’s Economic Growth by B. S. Saruk, G. Mokesh Rayalu

“…The main aim of this study is to create strong machine learning models and statistical techniques that can accurately predict crop yield by combining a variety of environmental parameters, then assess which models outperform each other. …”
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Deep learning for sunflower (Helianthus annuus L.) mapping and stand counting: Trade-offs between closed vs. open-access methods by Maria Villamil-Mahecha, Harsh Pathak, Nitin Rai, Paul Overby, Xin Sun

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Optimizing navigation and chemical application in precision agriculture with deep reinforcement learning and conditional action tree by Mahsa Khosravi, Zhanhong Jiang, Joshua R. Waite, Sarah E. Jones, Hernan Torres Pacin, Arti Singh, Baskar Ganapathysubramanian, Asheesh Kumar Singh, Soumik Sarkar

Subjects: “…Deep reinforcement learning…”
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