[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125967-en":3,"doc-seo-125967-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125967,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Application of machine learning algorithms for predicting agricultural crop yields","This article examines machine learning methods for predicting agricultural crop yields, with a focus on building interpretable models using the C4.5 decision-tree algorithm. The study analyzes a Kaggle dataset covering multiple crops and yield outcomes alongside rainfall, fertilizer usage, air temperature, and soil nitrogen, phosphorus, and potassium content. Correlation analysis highlights air temperature and soil nutrient levels as strongest yield drivers, while fertilizer and rainfall show weaker model influence. Evaluation on Deductor Studio reports high classification accuracy and motivates further improvement through larger datasets, advanced algorithms, and IoT-enabled monitoring.","Application of machine learning algorithms for predicting agricultural crop yields  \nVladislav Kukartsev1,2, Vasiliy Orlov2*, Vladimir Khramkov2, and Alyona Rozhkova3  \n1Reshetnev Siberian State of Science and Technology, Krasnoyarsk, Russia  \n2Bauman Moscow State Technical University, Artificial Intelligence Technology Scientific and Education Center, Moscow, Russia  \n3Agriculture Krasnoyarsk state agrarian university, Krasnoyarsk, Russia  \nAbstract. This article examines the use of machine learning algorithms for predicting the yield of agricultural crops. The primary classification method chosen is the C4.5 algorithm, which allows for the construction of interpretable models that identify key factors affecting yield. The analysis utilized data from a dataset available on the Kaggle platform, including information on various crops, their yields, and associated factors such as rainfall, fertilizer usage, air temperature, and the content of nitrogen, phosphorus, and potassium in the soil. The conducted correlation analysis showed that air temperature and the content of nitrogen, phosphorus, and potassium in the soil have the greatest impact on yield. Despite high correlation, the amount of fertilizer and rainfall were less significant in the model, indicating the need for further investigation of their influence. The model evaluation on the Deductor Studio platform demonstrated high classification accuracy, but there are opportunities for improvement. The importance of the results underscores the necessity for precise monitoring and management of key factors in agricultural practice to enhance productivity. Future research could focus on integrating larger datasets and more complex algorithms, as well as utilizing Internet of Things (IoT)  \nsystems for more accurate monitoring and yield prediction.  \n1 Introduction  \nModern agriculture faces numerous challenges such as climate change, population growth, and the need to increase productivity. One of the ways to address these issues is through the use of data and machine learning methods to predict crop yields. Yield prediction enables farmers and policymakers to make informed decisions about crop selection, resource allocation, and logistics planning. Machine learning (ML) is becoming an increasingly important tool in this area, providing high accuracy and reliability in forecasts.  \nYield prediction is especially crucial in the context of climate change, where traditional experience-based methods are becoming less reliable [1-3] . In this study, the C4.5 algorithm was used to build classification models. C4.5 is one of the most popular and effective implementations of decision trees. This algorithm allows for the creation of interpretable  \n* [Corresponding author:](Corresponding author: vasi4244@gmail.com)[ ](Corresponding author: vasi4244@gmail.com)[vasi4244@gmail.com](Corresponding author: vasi4244@gmail.com)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nmodels that can be used to understand the factors affecting yield and to make informed decisions in agriculture.  \nYield prediction allows farmers to make more informed decisions regarding sowing and harvesting operations, as well as optimizing resource use. This is particularly important in the context of climate change, where traditional experience-based methods are becoming less reliable [4-6] .  \nVarious machine learning algorithms demonstrate high efficiency in yield prediction tasks. Among them are:  \n• C4.5 Algorithm: A fundamental algorithm for building decision trees, widely used for classification and regression in yield prediction tasks [7-8] .  \n• Artificial Neural Networks (ANNs): A popular algorithm capable of handling complex and nonlinear dependencies, making it particularly effective for yield prediction [9-","cbCail1iCX6jVetF","https://ap.wps.com/l/cbCail1iCX6jVetF","pdf",1893839,10,1,7,"English","en",105,"# Introduction\n## Materials and methods\n### Data Sources\n### Data Preprocessing","[{\"question\":\"Which machine learning algorithm is used to build the yield prediction models?\",\"answer\":\"The study uses the C4.5 algorithm to construct interpretable classification models for crop yield prediction.\"},{\"question\":\"What factors are found to have the greatest impact on yield?\",\"answer\":\"Correlation results indicate that air temperature and the soil contents of nitrogen, phosphorus, and potassium have the strongest impact on yield.\"},{\"question\":\"How was the model performance evaluated?\",\"answer\":\"Model evaluation was conducted on the Deductor Studio platform, which demonstrated high classification accuracy.\"}]","Application of machine learning algorithms for predicting agricultural crop yields | 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machine learning algorithm is used to build the yield prediction models?","Question",{"text":77,"@type":78},"The study uses the C4.5 algorithm to construct interpretable classification models for crop yield prediction.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What factors are found to have the greatest impact on yield?",{"text":82,"@type":78},"Correlation results indicate that air temperature and the soil contents of nitrogen, phosphorus, and potassium have the strongest impact on yield.",{"name":84,"@type":75,"acceptedAnswer":85},"How was the model performance evaluated?",{"text":86,"@type":78},"Model evaluation was conducted on the Deductor Studio platform, which demonstrated high classification 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