Enhancing Agricultural Productivity through Machine Learning: A Model for Accurate Crop Yield Prediction by adlin jebaumari, jayanthila devi

“…The primary objective is to utilise machine learning techniques to forecast crop yield accurately, thereby aiding farmers in making informed decisions to optimize agricultural output and ensure food security.In the majority of developing countries, agriculture is the backbone of the economy. …”
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Sugarcane yield forecasting using machine learning techniques in Udham Singh Nagar district of Uttarakhand by NEHA CHAND, RAJEEV RANJAN

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Winter Wheat Yield Prediction Using Satellite Remote Sensing Data and Deep Learning Models by Hongkun Fu, Jian Lu, Jian Li, Wenlong Zou, Xuhui Tang, Xiangyu Ning, Yue Sun

“…This study proposes a method for predicting yields in China’s major winter wheat-producing regions using MOD13A1 data and a deep learning model which incorporates an Improved Gray Wolf Optimization (IGWO) algorithm. …”
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A deep learning framework for prediction of crop yield in Australia under the impact of climate change by Haydar Demirhan

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Maize and soybean yield prediction using machine learning methods: a systematic literature review by Ramandeep Kumar Sharma, Jasleen Kaur, Gary Feng, Yanbo Huang, Chandan Kumar, Yi Wang, Sandhir Sharma, Johnie Jenkins, Jagmandeep Dhillon

“…Abstract Today’s agronomy is data-rich, and machine learning (ML) provides the ability to efficiently predict crop yields, utilizing high-volume data to optimize agricultural decision-making. …”
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Seed quality drives grain yield in Ethiopian and Senegalese sorghum: Insights from machine learning by Ezekiel Ahn, Louis K. Prom, Jae Hee Jang, Insuck Baek, Adama R. Tukuli, Seunghyun Lim, Seok Min Hong, Moon S. Kim, Lyndel W. Meinhardt, Sunchung Park, Clint Magill

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Applications of machine learning and deep learning in agriculture: A comprehensive review by Muhammad Waqas, Adila Naseem, Usa Wannasingha Humphries, Phyo Thandar Hlaing, Porntip Dechpichai, Angkool Wangwongchai

Subjects: “…Machine learning…”
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Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice Yield by Nisha P. Shetty, Balachandra Muniyal, Ketavarapu Sriyans, Kunyalik Garg, Shiv Pratap, Aman Priyanshu, Dhruthi Kumar

“…The increased integration of Deep Learning (DL) and Machine Learning (ML) into agriculture has enabled substantial advancements in predicting crop yields and analyzing factors affecting them. …”
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Impact of data spatial resolution on barley yield prediction mapping by F. Ksantini, M. Quemada, N. Arencibia-Pérez, A.F. Almeida-Ñauñay, E. Sanz, Ana M. Tarquis

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Assessing climate and land use impacts on surface water yield using remote sensing and machine learning by Amanuel Kumsa Bojer, Muluneh Woldetsadik Abshare, Fitsum Mesfin, Ayad M. Fadhil Al-Quraishi

“…An ensemble of machine learning models, including Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB), were used to evaluate the effects of climate variability and land use on annual water yield. …”
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Cereal and Rapeseed Yield Forecast in Poland at Regional Level Using Machine Learning and Classical Statistical Models by Edyta Okupska, Dariusz Gozdowski, Rafał Pudełko, Elżbieta Wójcik-Gront

“…Various models were employed, including machine learning algorithms and multiple linear regression. …”
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Grape vine (Vitis vinifera) yield prediction using optimized weighted ensemble machine learning approach by Nobin Chandra Paul, Pratapsingh S. Khapte, Navyasree Ponnaganti, Sushil S. Changan, Sangram B. Chavan, K. Ravi Kumar, Dhananjay D. Nangare, K. Sammi Reddy

“…In this study, we propose an optimized weighted ensemble machine learning approach for predicting grape vine yield, integrating multiple morphological, physiological, and berry quality parameters. …”
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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

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Machine Learning for Precision Agriculture: Predicting Persimmon Peak Harvest Dates and Yield Using Meteorological Data by Atsushi Okayama, Atsushi Yamamoto, Yutaka Matsuno, Masaomi Kimura

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Robust County-Level Corn Yield Estimation Using Ensemble Machine Learning and Multisource Remote Sensing by Alireza Vafaeinejad, Alireza Sharifi, Shahid Nawaz Khan

“…These findings demonstrate the potential of ensemble learning models to deliver reliable and interpretable yield forecasts, even under imperfect data conditions, making them practical tools for real-world precision agriculture applications. …”
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Enhancing prediction of crop yield and soil health assessment for sustainable agriculture using machine learning approach by Kapil Netaji Vhatkar, Shweta Ashish Koparde, Sonali Kothari, Jayesh Sarwade, Kishor Sakur

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Optimizing potato yield predictions in Uttar Pradesh, India: a comparative analysis of machine learning models by Ahmad Alsaber, Anurag Satpathi, Mariam Alsabah, Parul Setiya

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Handling Sensor Faults in Hydroponics: A Deep Learning Imputation Technique for Accurate Tomato Yield Prediction by Viji Venugopal, Paresh Tanna, Ramesh Karnati

“…Sensor faults in hydroponic systems pose significant challenges for precision agriculture by compromising the nutrient monitoring accuracy and yield prediction reliability. Current imputation methods lack domain-specific agricultural pattern-preservation capabilities. …”
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Stacking Ensemble Machine Learning Modelling for Milk Yield Prediction Based on Biological Characteristics and Feeding Strategies by Ruiming Xing, Baihua Li, Shirin Dora, Michael Whittaker, Janette Mathie

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Deep learning-based semantic segmentation for rice yield estimation by analyzing the dynamic change of panicle coverage by Hyeok-Jin Bak, Eun-Ji Kim, Ji-Hyeon Lee, Sungyul Chang, Dongwon Kwon, Woo-Jin Im, Woon-Ha Hwang, Jae-Ki Chang, Nam-Jin Chung, Wan-Gyu Sang

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