Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables by Biplob Dey, Jannatul Ferdous, Romel Ahmed

“…Machine learning (ML) can make use of agricultural data related to crop yield under varying soil nutrient levels, and climatic fluctuations to suggest appropriate crops or supplementary nutrients to achieve the highest possible production. …”
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Sugarcane Biomass Prediction with Multi-Mode Remote Sensing Data Using Deep Archetypal Analysis and Integrated Learning by Zhuowei Wang, Yusheng Lu, Genping Zhao, Chuanliang Sun, Fuhua Zhang, Su He

“…Since feature interpretability in agricultural data mining is significant, a feature extraction method of deep archetypal analysis (DAA) that has good model interpretability is introduced and aided by principal component analysis (PCA) for feature mining from the multi-mode multispectral and light detection and ranging (LiDAR) remote sensing data pertaining to sugarcane. …”
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Big Data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability by Mario San Emeterio de la Parte, José-Fernán Martínez-Ortega, Vicente Hernández Díaz, Néstor Lucas Martínez

“…The paper concludes with a distributed framework for managing complex spatio-temporal semantic data by offering service interoperability through data integration to external agricultural data models. Graphical Abstract…”
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Factors Shaping Performance of Polish Biodiesel Producers Participating in the Farm Accountancy Data Network in the Context of the Common Agricultural Policy of the European Union by Aneta Bełdycka-Bórawska, Piotr Bórawski, Lisa Holden, Tomasz Rokicki, Bogdan Klepacki

“…The FADN is the most comprehensive and detailed source of economic and agricultural data in Poland. Changes in the number of biodiesel producers in the Polish market were analyzed with the use of indicators. …”
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A dataset cataloging product-specific human appropriation of net primary production (HANPP) in US counties by Suman Paudel, Kaeli Mueller, Gustavo Ovando-Montejo, Lauren Tango, Richard Rushforth, Christopher Lant

“…To calculate HANPP, raw agricultural data were downloaded from public databases such as USDA-National Agricultural Statistics Service Quick Stats and Cropland Data Layer, US Forest Service Timber Product Output, and NPP data from MODIS. …”
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Spatio-Temporal Semantic Data Model for Precision Agriculture IoT Networks by Mario San Emeterio de la Parte, Sara Lana Serrano, Marta Muriel Elduayen, José-Fernán Martínez-Ortega

“…In addition, an architecture for the management of spatio-temporal semantic agricultural data in real-time is proposed. This architecture results in the DAM&DQ system responsible for data management as semantic middleware on the AFarCloud platform. …”
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Channel Attention GAN-Based Synthetic Weed Generation for Precise Weed Identification by Tang Li, Motoaki Asai, Yoichiro Kato, Yuya Fukano, Wei Guo

“…Overall, the experimental results demonstrated that the proposed method outperformed previous state-of-the-art GAN models in terms of image quality, diversity, and discriminability, making it a promising approach for synthetic agricultural data generation.…”
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Combined Effects of Heat and Drought Stress on the Growth Process and Yield of Maize (<i>Zea mays</i> L.) in Liaoning Province, China by Wenying Yu, Ruipeng Ji, Jinwen Wu, Rui Feng, Na Mi, Nina Chen

“…The spatial and temporal characteristics of CHD and its effects on maize were evaluated based on daily meteorological data at 52 meteorological stations in Liaoning from 1961 to 2020, as well as agricultural data including details of the maize development periods. …”
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Precision farming practices with data-driven analysis and machine learning-based crop and fertiliser recommendation system by Vijender Reddy G., Venkata Krishna Reddy M., Spandana K., Subbarayudu Yerragudipadu, Albawi Ali, Chandrashekar Rakesh, Singla Atul, Praveen

“…Machine Learning (ML) is employed for agricultural data analysis. The goal of this research is to optimise agricultural practices by presenting an integrated crop and fertiliser recommendation system. …”
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AD|ARC (Administrative Data | Agricultural Research Collection): Linking individual, household and farm business data for agricultural research – Challenges of linking agricultural... by Sian Morrison-Rees, Nicholas Webster, Paul Caskie

“…To the best of our knowledge, these are the first datasets linking agricultural data to individual- and household-level data at a population level. …”
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Regional Inventory Approach for Paddy Plantation Area Estimation at Yogyakarta Special Province by Noorhadi Noorhadi, Su Ritohardoyo

“…Generally agricultural data could be collected by several techniques, i.e. rural statistic, sensus, administrative by product, multiple frame sampling  surcey, and frame sampling survey regional inventory. …”
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Detection of the Spatio-Temporal Differentiation Patterns and Influencing Factors of Wheat Production in Huang-Huai-Hai Region by Yifan Zhang, Bingjun Li

“…To this end, this study first conducted a quantitative analysis of the yield differentiation patterns in Huang-Huai-Hai (HHH) wheat based on the 2010–2020 wheat agricultural data, comprehensively using the Theil index and exploratory spatial data analysis. …”
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Agricultural Knowledge Recommendation Model Integrating Time Perception and Context Filtering by WANG Pengzhe, ZHU Huaji, MIAO Yisheng, LIU Chang, WU Huarui

“…When both time-aware and augmented filtering modules were introduced, the model performance was further improved, which on the one hand illustrated the dependence of the augmented filtering module on the time-aware module, and on the other hand verified the necessity of adopting the augmented filtering to the time-aware self-attention network model.ConclusionsThis research proposes an agricultural knowledge recommendation model that integrates time-awareness and augmented filtering, which introduces the user's interaction time interval into the embedded information, so that the model effectively learns the information of agricultural time in the agricultural scene, and the prediction of the user's interaction time and the object is more closely related to the actual scene; augmented filtering algorithms are used to attenuate the noise in the agricultural data. At the same time, the enhanced filtering algorithm is used to attenuate the noise in the agricultural data, and can be effectively integrated into the model for use, further improving the recommendation performance of the model. …”
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Data Mining and NIR Spectroscopy in Viticulture: Applications for Plant Phenotyping under Field Conditions by Salvador Gutiérrez, Javier Tardaguila, Juan Fernández-Novales, Maria P. Diago

“…In this context, data mining strategies may be applied to agricultural data retrieved with new non-invasive devices, with the aim of yielding useful, reliable and objective information. …”
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Spatial correlations and driving mechanisms of low-carbon agricultural development in china by Fang Fang, Fang Fang, Jun Zhao, Jun Zhao, Jie Di, Lijie Zhang, Lijie Zhang

“…This paper used agricultural data from 30 Chinese provinces from 2001 to 2020, considering both carbon emissions from farming and livestock, agricultural low-carbon total factor productivity (ALTFP) was measured using the RSBM-GML index. …”
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Mapping the expansion of berry greenhouses onto Michoacán’s ejido lands, México by Sarah Hartman, Michelle Farfán, Jaime Hoogesteger, Paolo D’Odorico

“…We combine a national dataset on ejidal land (which includes both communal and parcel land) with geospatial agricultural data to quantify the land-use changes in six municipalities in the berry-growing region of Michoacán between 1989 and 2021. …”
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The natural and capital infrastructure of potential post-electrification wealth creation in Kenya by Diego Ponce de Leon Barido, Simone Fobi Nsutezo, Jay Taneja

“…NC data is composed of water body data (major rivers and access to irrigation infrastructure), soil data (soil quality and agro-ecological potential), and agricultural data (crop intensity and diversity). IC is composed of spatial data spanning major towns, first and second tier roads, electricity infrastructure (transmission grid, location of government, and entrepreneur run off-grid electrification projects), population density, access to education, trade centers (markets), healthcare, and access to financial services. …”
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Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data by Rosa Angela Pacheco-Gil, Ciro Velasco-Cruz, Paulino Pérez-Rodríguez, Juan Burgueño, Sergio Pérez-Elizalde, Francelino Rodrigues, Ivan Ortiz-Monasterio, David Hebert del Valle-Paniagua, Fernando Toledo

“…Abstract Background As a result of the technological progress, the use of sensors for crop survey has substantially increased, generating valuable information for modelling agricultural data. Plant spectroscopy jointly with statistical modeling can potentially help to assess certain chemical components of interest present in plants, which may be laborious and expensive to obtain by direct measurements. …”
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Influence of seasonal climatic water deficit and crop prices on rainfed crop grain harvest, repurposing, and abandonment in the western U.S.A. by Zachary H Lauffenburger, Marco P Maneta, Perry Miller, John S Kimball

“…We used historical climate and agricultural data between 1958 and 2020 to model crop repurposing (e.g. forage) across the observed CWD record using a fixed effect model. …”
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The Role of Earth Observation in Achieving Sustainable Agricultural Production in Arid and Semi-Arid Regions of the World by Sarchil Hama Qader, Jadu Dash, Victor A. Alegana, Nabaz R. Khwarahm, Andrew J. Tatem, Peter M. Atkinson

“…Secondly, it demonstrates how freely available RS data can support decision-making through a cost-effective monitoring system that complements traditional approaches for collecting agricultural data. Thirdly, it illustrates the challenges of employing freely available RS data for mapping and monitoring crop area, crop status and forecasting crop yield in these regions. …”
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