Search Results - "yield prediction"

Rice is an important crop in Nepal. Despite being an important crop, several challenges have led to the increasing rice imports in the country. A constraint still remains in the proper rice yield estimation mechanisms still unapplied. Remote sensing technologies have been successfully deployed in...

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This publication aims to introduce readers to recent crop yield forecasting approaches based on artificial intelligence (AI) and to provide examples of how AI can potentially improve yield forecasting at the field and regional levels. Written by Clyde Fraisse, Yiannis Ampatzidis, Sandra Guzmán, Won...

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Accurate and timely wheat yield prediction is valuable globally for enhancing agricultural planning, optimizing resource use, and supporting trade strategies. Study addresses the need for precision in yield estimation by applying machine-learning (ML) regression models to high-resolution Unmanned Ae...

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Strawberry, a vital crop in horticulture, faces challenges like pest infestations and climate variability that affect stable production. A crop model based on photosynthesis-derived dry matter (DM) production is an effective method to examine the environment–plant growth relationship. The developed...

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<b>Background:</b> Soybean is an important global crop in food security and agricultural economics. Accurate estimation of soybean pod counts is critical for yield prediction, breeding programs, precision farming, etc. Traditional methods, such as manual counting, are slow, labor-intensi...

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Accurate milk yield prediction is essential for effective dairy herd management, particularly in smallholder dairy where daily milk recording is often limited. Predictive models that can estimate total milk yield during one lactation period using early-stage recording data offer a practical solution...

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The use of artificial intelligence tools in agriculture is growing. In particular, the use of ensemble models. However, there are still few reviews on the use of these models in the study of the impacts of climate change on agriculture. Therefore, the aim of this article is to review the use of such...

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Accurate pre-harvest prediction of yield and quality (tenderometric reading, TR) is crucial for the processing pea industry due to a narrow optimal harvest window. Machine learning (ML) models offer potential, but their real-world utility depends on their performance stability across different years...

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Satellite remote sensing (RS) offers an efficient, large-scale approach for monitoring crop health, particularly in precise estimation of crop yields. Rice is a staple food for over three billion people worldwide, making it crucial to estimate rice yield promptly to ensure food security and support...

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Reliable early-stage yield forecasts are essential in precision viticulture, enabling timely interventions such as harvest planning, canopy management, and crop load regulation. Since grape yield is directly related to the number and size of bunches, the early detection of inflorescences and bunches...

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This study focuses on enhancing crop yield prediction in plant factory environments through precise crop canopy image capture and background interference removal. This method achieves highly accurate recognition of the crop canopy projection area (CCPA), with a coefficient of determination (R<sup...

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The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning. Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data, it is not clear how different models can be used to effe...

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The digitalization of agriculture has increasingly integrated artificial intelligence (AI), machine learning (ML), and deep learning (DL) to address the challenges arising from population growth, climate change (CC), and resource limitations. This study provides a comprehensive review of the potenti...

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Sugarcane yield prediction plays a pivotal role in enabling farmers to monitor crop development and optimize cultivation practices, guiding harvesting operations for sugar mills. In this study, we established three experimental fields, which were planted with three main sugarcane cultivars in Guangx...

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Maize (Zea mays L.) stands prominently as one of the major cereal crops in China as well as in the rest of the world. Therefore, predicting the growth and yield of maize for large areas through yield components under high-yielding environments will help in understanding the process of yield formatio...

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Variation in phenological stage is the major nonlinearity in monitoring, modeling and various estimations of agricultural systems. Indices are used as a common means of evaluating agricultural monitoring data from remote sensing and terrestrial observation systems, and many of these indices have lin...

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In precision agriculture research, it is important to monitor maize growth conditions in real time for effective field diagnosis, management, and accurate yield prediction. This study aimed to develop an inverse model of vegetation index and maize yield using Unmanned Aerial Vehicle (UAV) multispect...

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Rice yield prediction is critical for ensuring food security, particularly in major rice-producing countries like China. While Unmanned Aerial Vehicles (UAVs) equipped with hyperspectral imaging are widely used for yield prediction due to its ability to capture detailed spectral information, they ma...

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Yield prediction in wheat cultivated under semi-arid climatic conditions is gaining increasing importance for sustainable production strategies and decision support systems. In this study, a time-series-based modeling approach was implemented using sensor-based data (SPAD, NSPAD, NDVI, INSEY, and pl...

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The application of time-series analysis in agricultural yield forecasting has gained much attention in recent years with the use of machine learning models. However, the utilization of Python-based time-series models for predicting oil palm yield remains limited. This study aims to explore the poten...

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