A sustainable yield prediction in agricultural fields is a very critical task that aims to help farmers have an idea about agricultural operations. Generally, we can find a variety of applications proposed for this purpose that include fruit counting...
Sustainable agricultural planning requires predictive frameworks that can capture spatiotemporal variability, sustainability dynamics, and the potential outcomes of alternative management scenarios. The research proposes TSAFI-DT, a retrospectively v...
Accurate crop yield prediction is paramount for food security amid climate volatility but struggles with complex, nonlinear, and spatially heterogeneous weather–crop interactions. This study develops a novel Spatially Heterogeneous Functional A...
The innovation in science and technical knowledge has prompted an enormous amount of information for the agrarian sector. Machine learning has risen with massive processing techniques to perceive new contingencies in agricultural development. Machine...
This research introduces the PEnsemble 4 model, a weighted ensemble prediction model that integrates multiple individual machine learning models to achieve accurate maize yield forecasting. The model incorporates unmanned aerial vehicle (UAV) imagery...
The acute shortage of agricultural labor necessitates the development of predictive models to optimize farm operations. This study investigates the effectiveness of artificial-intelligence-driven models to accurately forecast the timing and yield of...
This bibliometric study maps olive (Olea europaea L.) yield prediction research as a coherent scientific domain for the first time. A Scopus query (27 February 2026) yielded 84 peer-reviewed articles (2002–2025), from which co-authorship networ...
A crop yield prediction is critical to increase agricultural sustainability because it allows for the more effective use of natural resources, including water, fertilizers, and soil. Accurate yield estimates enable farmers and governments to more acc...
Spatial variability in a crop field creates a need for precision agriculture. Economical and rapid means of identifying spatial variability is obtained through the use of geotechnology (remotely sensed images of the crop field, image processing, GIS...
Accurate crop yield prediction is crucial for formulating agricultural policies, guiding agricultural management, and optimizing resource allocation. This study proposes a method for predicting yields in China’s major winter wheat-producing reg...
The long term archiving of both Landsat imagery and wheat yield mapping datasets sensed by precision agriculture technology has the potential through the development of statistical relationships to predict high resolution estimates of wheat yield ove...
Wheat is one of the main grain species as well as one of the most important crops, being the basic food ingredient of people and livestock. Due to the importance of wheat production scale, it is advisable to predict its yield before harvesting. Howev...
The accurate prediction of crop yields is crucial for enhancing agricultural efficiency and ensuring food security. This study assesses the performance of the CNN-LSTM-Attention model in predicting the yields of maize, rice, and soybeans in Northeast...
Precise prediction of wheat yield plays a crucial role in food security and resource management in Pakistan. The current research suggests an artificial intelligence-driven framework based on 23 years of agro-meteorological and yield data that predic...
Accurate and timely crop yield prediction is crucial for global food security and modern agricultural management. Traditional methods often lack the scalability and granularity required for precision farming. This paper introduces FARM (Fine-tuning A...
Litchi (Litchi chinensis Sonn.) is a pillar of the tropical agricultural economy in southern China, yet its production faces increasing instability due to climate change. Traditional agronomic models often fail to capture the complex, non-linear inte...
Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a...
Accurate agricultural yield prediction is crucial for crop management and data-driven decision-making. This study proposes a machine learning framework for predicting total lentil seed yield using 264 observations collected during a single growing se...
The sustainability of agricultural production in the twenty-first century, both in industrialized and developing countries, benefits from the integration of farm management with information technology such that individual plants, rows, or subfields m...
A farmer can use machine learning to make decisions about what crops to sow, how to care for those crops throughout the growing season, and how to predict crop yields. According to the World Health Organization, agriculture is essential to the nation...
Recent innovations are increasingly recognizing applications in precision agricultural systems that use data science techniques as well as so-called machine learning techniques. Big data analytics have created various data-intensive decision-making o...
The efficient prediction of corn biomass yield is critical for optimizing crop production and addressing global challenges in sustainable agriculture and renewable energy. This study employs advanced machine learning techniques, including Gradient Bo...
Accurate crop yield prediction provides essential information for strategic decision-making and precision agriculture, yet achieving reliable forecasts across unseen growing seasons remains challenging. The main objective of this study is to develop...
Agricultural development is one of the most essential needs worldwide. In Norway, the primary foundation of grain production is based on geological and biological features. Existing research is limited to regional-scale yield predictions using artifi...
Chickpea (Cicer arietinum L.) is a globally important legume crop whose grain yield is strongly influenced by environmental and agronomic variability. This study aimed to predict chickpea grain yield using artificial neural networks (ANNs) and to ide...
Early prediction of common bean (Phaseolus vulgaris L.) yield is essential for improving productivity in tropical agricultural systems. In this study, we integrated canopy structural metrics obtained with the Tracing Radiation and Architecture of Can...
Adverse weather conditions, once rare anomalies, are now becoming increasingly commonplace, causing heavy losses to crops and livestock. One of the most immediate and far-reaching concerns is the potential impact on agricultural productivity and glob...
Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers’ experiences,...
Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the ap...
Context: We are advancing our efforts in agriculture by creating a crop prediction system with the help of machine learning. Our goal is to build an ML model that can estimate the properties of a crop. It will push ahead in agriculture by developing...
Accurate crop yield prediction is fundamental to sustainable agricultural management, enabling optimized resource allocation and informed decision-making. However, a critical gap exists in current prediction models: existing approaches overlook the t...
A wheat growth and counting analysis model based on instance segmentation is proposed in this study to address the challenges of wheat growth monitoring and yield prediction in high-density agricultural environments. The model integrates the transfor...
Accurate crop yield forecasting is essential for ensuring food security, optimizing agricultural resource allocation, and supporting climate-resilient farming systems. Recent advances in deep learning have improved yield prediction accuracy; however,...
Accurate prediction of winter oilseed rape yield is essential for optimising crop management and improving production efficiency. However, the reliability of commonly reported model performance remains uncertain due to the widespread use of random va...
This study, employing the AquaCrop model, demonstrated notable efficacy in assessing and predicting crop yields for winter wheat, maize, winter rapeseed, and sugar beets in the Joint Experiment for Crop Assessment and Monitoring (JECAM) test area of...
Timely forecasting of maize productivity is essential to support precision agriculture and optimize management practices. In this study, we analyzed the potential of integrating ground-based measurements and UAV-derived spectral data for predicting m...
This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and y...
The operational limitations of ground-based machinery in waterlogged soils of northern regions often lead to missed agronomic treatments, resulting in substantial yield losses. This problem is important in potato production, where a delay in disease...
Lettuce, a vital economic crop, benefits significantly from intelligent advancements in its production, which are crucial for sustainable agriculture. Deep learning, a core technology in smart agriculture, has revolutionized the lettuce industry thro...
Climate change is a major hazard to the agricultural systems of the world, as it is changing the temperature regimes, precipitation patterns, and soil dynamics, which are weakening crop production and the stability of ecosystems. The proposed researc...
The aim of the work was to produce three independent, multi-criteria models for the prediction of winter rapeseed yield. Each of the models was constructed in such a way that the yield prediction can be carried out on three dates: April 15th, May 31s...
This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural la...
This study provides a comparative evaluation of several ensemble model constructions for the prediction of specific methane yield (SMY) from anaerobic digestion. From the authors’ knowledge based on existing research, present knowledge of their...
Predicting crop yields is one of the most challenging tasks in agriculture. It plays an essential role in decision making at global, regional, and field levels. Soil, meteorological, environmental, and crop parameters are used to predict crop yield....
As extreme weather events increasingly threaten global food systems, accurately assessing climate risks and predicting regional crop yields remains a critical challenge. Conventional prediction models often rely on direct weather-to-yield relationshi...
Global food security faces escalating threats from climate variability and resource constraints. Accurate crop yield forecasting is essential; however, existing methods frequently overlook complex spatial dependencies driven by climate teleconnection...
The paper presents AgriMicro, a modern Farm Management Information System (FMIS) designed to help farmers monitor and optimize corn crops from sowing to harvest, by leveraging cloud technologies and machine learning algorithms. The platform is built...
The red-edge (RE) spectral region has become a central component of agricultural remote sensing because it captures physiologically meaningful changes in chlorophyll content, canopy structure and vegetation functioning. The availability of dedicated...
With growing global demand for renewable energy and the need for efficient use of organic waste, agricultural waste (including animal excrement and plant residues) is considered a key and promising feedstock for biogas production via anaerobic digest...
This study presents an innovative unmanned aerial vehicle (UAV)-based intelligent detection method utilizing an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture to address the inefficiency and inaccuracy inherent...
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