Search Results - "algorithm"

Spatially explicit regionalization of airborne flux measurements using environmental response functions by Metzger, S., Junkermann, W., Mauder, M., Butterbach-Bahl, Klaus, Trancon y Widemann, B., Neidl, F., Schafer, K., Wieneke, S., Zheng, X.H., Schmid, H.P., Foken, T.

“…This study indicates the potential of ERFs for (i) extending airborne flux measurements to the catchment scale, (ii) assessing the spatial representativeness of long-term tower flux measurements, and (iii) designing, constraining and evaluating flux algorithms for remote sensing and numerical modelling applications.…”
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A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing by Biradar, Chandrashekhar M., Thenkabail, Prasad S., Noojipady, P., Li, Yuan Jie, Dheeravath, Venkateswarlu, Turral, Hugh, Velpuri, Naga Manohar, Gumma, Murali K., Gangalakunta, O.R.P., Cai, X.L., Xiao, X., Schull, M.A., Alankara, Ranjith, Gunasinghe, Sarath, Mohideen, Sadir

“…These consist of: (a) data fusion and composition of multi-resolution time-series mega-file data-cube (MFDC), (b) image segmentation based on precipitation, temperature, and elevation zones, (c) spectral correlation similarity (SCS), (d) protocols for class identification and labeling through uses of SCS R2-values, bi-spectral plots, space-time spiral curves (ST-SCs), rich source of field-plot data, and zoom-in-views of Google Earth (GE), and (e) techniques for resolving mixed classes by decision tree algorithms, and spatial modeling. The outcome was a 9-class GMRCA from which country-by-country rainfed area statistics were computed for the end of the last millennium. …”
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Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru by Enriquez Pinedo, Lucia Carolina, Ortega Quispe, Kevin Abner, Ccopi Trucios, Dennis, Rios Chavarria, Claudia Sofía, Urquizo Barrera, Julio, Patricio Rosales, Solanch Rosy, Alejandro Mendez, Lidiana Rene, Oliva Cruz, Manuel, Barboza Castillo, Elgar, Pizarro Carcausto , Samuel Edwin

“…A UAV-captured image was used to predict the spatial distribution of soil parameters, generating fourteen spectral indices and a digital surface model (DSM) from 103 soil plots across 49.83 hectares. Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). …”
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Decision support system for selecting the rootstock, irrigation regime and nitrogen fertilization in winemaking vineyards: WANUGRAPE4.0 by Visconti, Fernando, de-la-fuente, M., Buesa, Ignacio, Escalona, J. M, Medrano Gil, Hipólito, Miranda, C., Miras-Ávalos, José M., Palacios, Javier, Pérez-Pérez, Juan G., Ramírez-Cuesta, Juan M., Romero-Azorín, Pascual, Santesteban, L. G., Torres, N., Uriarte, David, Parra, M., Intrigliolo, Diego S.

“…First, the modular structure and information flow of the DSS has been defined. Second, the main algorithms of the water balance module (DSS core part) have been formulated and the module coded in a spreadsheet. …”
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Detection of subsurface bruises in plums using spectral imaging and deep learning with wavelength selection by Castillo-Gironés, Salvador, Van Bellenghem, R., Wouters, N., Munera, Sandra, Blasco, José, Saeys, W.

“…Therefore, this study aimed to explore the potential of hyperspectral imaging in the 430 to 1 000 nm range and deep learning algorithms to detect these invisible bruises at an early stage. …”
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Virus detection by high-throughput sequencing of small RNAs: large-scale performance testing of sequence analysis strategies by Massart, S., Chiumenti, M., Jonghe, K., Glover, R., Haegeman, A., Koloniuk, I., Kominek, P., Kreuze, Jan F., Kutnjak, D., Lotos, L., Maclot, F., Maliogka, V., Maree, Hans J.

“…Many different bioinformatics algorithms aimed at detecting viruses in HTS data have been reported but little attention has been paid thus far to their sensitivity and reliability for diagnostic purposes. …”
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Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling by Akpoti, K., Kabo-bah, A.T., Dossou-Yovo, Elliott Ronald, Groen, T.A., Zwart, Sander J.

“…In the present study, we developed an ensemble model approach to characterize the IVs suitability for rainfed lowland rice using 4 machine learning algorithms based on environmental niche modeling (ENM) with presence-only data and background sample, namely Boosted Regression Tree (BRT), Generalized Linear Model (GLM), Maximum Entropy (MAXNT) and Random Forest (RF). …”
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Insights into the genetic architecture of complex traits in Napier grass (Cenchrus purpureus) and QTL regions governing forage biomass yield, water use efficiency and feed quality... by Muktar, Meki S., Habte, Ermias, Teshome, Abel, Assefa, Yilikal, Negawo, Alemayehu T., Ki-Won Lee, Jiyu Zhang, Jones, Christopher S.

“…A genome-wide association study (GWAS), using two different mixed linear model algorithms implemented in the GAPIT R package, identified more than 35 QTL regions and markers associated with agronomic, morphological, and water-use efficiency traits. …”
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Molecular survey of cattle ticks in Burundi: First report on the presence of the invasive Rhipicephalus microplus tick by Nyabongo, Lionel, Odongo, David O., Milton, G., Machuka, Eunice M., Vudriko, P., Pelle, Roger, Kanduma, E.G.

“…Phylogenetic relationships were inferred using bayesian and maximum likelihood algorithms. A total of 483 ticks were collected from the five AEZs sampled. …”
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Coupling remote sensing and hydrological model for evaluating the impacts of climate change on streamflow in data-scarce environment by Akhtar, F., Awan, Usman Khalid, Borgemeister, C., Tischbein, B.

“…Most of the biophysical parameters required for the SWAT model were derived from remote sensing-based algorithms. The SUFI-2 technique was used for calibrating and validating the SWAT model with streamflow data. …”
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Viewpoint: irrigation water management in a space age by Hafeez, Mohsin, Awan, Usman Khalid

“…In the twenty-first century, considerable advances have been made in using satellite imagery, including processing and geospatial algorithms, to estimate hydro-meteorological fluxes and relevant components at different spatial scales. …”
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Data-driven similar response units for agricultural technology targeting: An example from Ethiopia by Tamene, Lulseged D., Abera, Wuletawu, Bendito, Eduardo, Erkossa, Teklu, Tariku, Meklit, Sewnet, Habtamu, Tibebe, Degefie, Sied, Jema, Feyisa, Gudina, Wondie, Menale, Tesfaye, Kindie

“…We used unsupervised machine learning algorithms to identify areas of high similarity or homogeneous zones called ‘SRUs’ that can guide the targeting of agricultural technologies. …”
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Novel insights into factors associated with yield response and nutrient use efficiency of maize and rice in sub-Saharan Africa. A review by Zingore, S., Adolwa, Ivan S., Njoroge, Samuel M., Johnson, J.M., Saito, Kazuki, Phillips, S., Kihara, Job Maguta, Mutegi, J., Murell, S., Dutta, S., Chivenge, Pauline P., Amouzou, K.A., Oberthür, Thomas, Weldesemayat, G.

“…Explorative analysis using machine learning algorithms provided further insights into the possible interaction of agroecology, soil type, and exchangeable cations on the spatial variability in yield responses to N, P, and K in maize and rice. …”
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Prediction of functional characteristics of gari (cassava flakes) using near-infrared reflectance spectrometry by Adesokan, Michael, Alamu, Emmanuel Oladeji, Fawole, S., Maziya-Dixon, Busie

“…Calibration models were developed using partial least regression algorithms after spectra preprocessing. Also, the gari samples were analysed in the laboratory for their functional properties to generate reference data. …”
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Exploring the agronomic performance and molecular characterization of diverse spring durum wheat germplasm in Kazakhstan by Tajibayev, Daniyar, Mukin, Kadyrzhan, Babkenov, Adylkhan, Chudinov, Vladimir, Dababat, Abdelfattah A., Jiyenbayeva, Karlyga, Kenenbayev, Serik, Savin, Timur, Shamanin, Vladimir, Tagayev, Kuttymurat, Rsymbetov, Askhat, Yessimbekova, Minura, Yusov, Vadim, Zhylkybaev, Ruslan, Morgounov, Alexey I., Muhammad Tanveer Altaf, Muhammad Azhar Nadeem, Faheem Shehzad Baloch

“…Marker-based cluster analysis, including STRUCTURE and neighbor-joining algorithms, divided the material into two populations with clear differences in geographic origin. …”
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Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains by Ahmed, Zia Uddin, Krupnik, Timothy J., Timsina, Jagadish, Saiful Islam, Hossain, M. Khaled, Alanuzzaman Kurishi, A.S.M., Shah-Al Emran, Harun-Ar-Rashid, McDonald, Andrew, Gathala, Mahesh K.

“…Interpretable machine learning (ML) algorithms in automatic machine learning (AutoML) frameworks were subsequently used to predict attainable yield relative nutrient-limited yield (RY) and to rank variables that control RY. …”
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Hybrid-AI and Model Ensembling to Exploit UAV-Based RGB Imagery: An Evaluation of Sorghum Crop’s Nitrogen Content by Hammouch, Hajar, Patil, Suchitra, Choudhary, Sunita, El-Yacoubi, A. Mounim, Masner, Jan, Kholová, Jana, Anbazhagan, Krithika, Vanek, Jirí, Qin, Huafeng, Stoces, Michal, Berbia, Hassan, Jagarlapudi, Adinarayana, Chandramouli, Magesh, Mamidi, Srinivas, Prasad, K.V.S.V., Baddam, Rekha

“…Our findings underscore the superiority of hybrid and ensembled AI algorithms in these experiments. The MLP + CNN-VGG16 combination achieved the best accuracy (R2 = 0.733, MAE = 0.264 N% on an independent dataset). …”
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Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize by Costa-Neto, Germano, Crossa, José, Fritsche-Neto, Roberto

“…Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providingin-silicorealization of the genotype-environment combinations that must be phenotyped in the field. …”
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Technical workflow development for integrating drone surveys and entomological sampling to characterise aquatic larval habitats of Anopheles funestus in agricultural landscapes in... by Byrne, Isabel, Chan, Kallista, Manrique, Edgar, Lines, Jo, Wolie, Rosine Z., Trujillano, Fedra, Garay, Gabriel Jimenez

“…Further research using data collected in this study can enable the development of deep-learning algorithms for identifying An. funestus breeding habitats across rural agricultural landscapes in Côte d’Ivoire and the analysis of risk factors for these sites.…”
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Explainable machine learning driven nutrient recommendation for maize production in Malawi by Liben, Feyera, Kihara, Job, Abera, Wuletawu, AbebeMesfin, Tewodrofins, Homann-kee Tui, Sabine, Munthali, Moses, Munthali, Chandiona, Nalivata, Patson, Mzumara, Edward, Tuimene, Lulseged

“…This study aimed to (1) evaluate and compare the predictive performance of multiple machine learning algorithms for maize yield estimation in Malawi; (2) identify the most important yield-determining features through recursive feature elimination (RFECV) and SHAP-based interpretation; (3) examine interaction effects between key nutrient inputs and environmental variables using two-dimensional partial dependence plots; and (4) translate model outputs into site-specific nutrient management insights for precision agronomy in smallholder maize systems. …”
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