Search Results - "machine learning"

Prediction of pulse suitability in rice fallow areas using fuzzy AHP-based machine learning methods in Eastern India by Sahoo, Satiprasad, Singha, Chiranjit, Govind, Ajit

“…According to the findings, all machine learning (ML) techniques identified high-suitability zones in the districts of Murshidabad, Birbhum, Paschim Bardhaman, Paschim Medinipur, and Jhargram. …”
Get full text

Integrating Earth Observation, Machine Learning and Citizen Science to Map Prosopis juliflora Invasion in Semi-Arid Rangelands of Kenya by Cherotich, Fredah, Galgallo, Diba, Dhulipala, Ram, Whitbread, Anthony M, Paliwal, Ambica

Subjects: “…machine learning…”
Get full text

Acoustic response discrimination of phulae pineapple maturity and defects using factor analysis of mixed data and machine learning algorithms by Arwatchananukul, Sujitra, Chaiwong, Saowapa, Aunsri, Nattapol, Kittiwachana, Sila, Luengwilai, Kietsuda, Trongsatitkul, Tatiya, Mahajan, Pramod, Blasco, José, Rattapon, Saengrayap

“…All the physical, chemical, and acoustic properties were used to classify for maturity and defects using the factor analysis (FA) technique and machine learning (ML). Results showed that maturity was correctly classified at 84.0 % by all parameters, while elected non-destructive parameters (color, specific gravity, and stiffness coefficients) showed lower results for distinguishing pineapples. …”
Get full text
Get full text

Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production by Chaali, Nesrine, Ramírez Gomez, Carlos Manuel, Jaramillo Barrios, Camilo Ignacio, Garr´e, Sarah, Barrero, Oscar, Ouazaa, Sofiane, Calderon Carvajal, John Edinson

“…This research assessed the effectiveness of applying multivariate geostatistical analysis and unsupervised machine learning (UML) to geophysical and multispectral data through ECa, NDWI and NDVI indices, for delineating and validating the SSMZ at different crop cycles in five rice field of Tolima department-Colombia. …”
Get full text
Get full text
Get full text

Reference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0 by Ali, Ashenafi, Erkossa, Teklu, Gudeta, Kiflu, Abera, Wuletawu, Mesfin, Ephrem, Mekete, Terefe, Haile, Mitiku, Haile, Wondwosen, Abegaz, Assefa, Tafesse, Demeke, Belay, Gebeyhu, Getahun, Mekonen, Beyene, Sheleme, Assen, Mohamed, Regassa, Alemayehu, Selassie, Yihenew G., Tadesse, Solomon, Abebe, Dawit, Walde, Yitbarek, Hussien, Nesru, Yirdaw, Abebe, Mera, Addisu, Admas, Tesema, Wakoya, Feyera, Legesse, Awgachew, Tessema, Nigat, Abebe, Ayele, Gebremariam, Simret, Aregaw, Yismaw, Abebaw, Bizuayehu, Bekele, Damtew, Zewdie, Eylachew, Schulz, Steffen, Tamene, Lulseged D., Elias, Eyasu

Subjects: “…machine learning…”
Get full text

Ensemble machine learning prediction of drivers affecting rice and wheat yield, greenhouse gas emissions, and yield-scaled emissions in Bangladesh by CGIAR Research Program on Climate Change, Agriculture and Food Security

Subjects: “…machine learning…”
Get full text

Clarity in rangelands: Distinguishing invasive species from palatable forage, using satellite monitoring, environmental drivers, and machine learning classifiers by Paliwal, Ambica, Mhelezi, M., Galgallo, Diba, Banerjee, Rupsha R., Whitbread, Anthony

Get full text

Reference soil groups map of Ethiopia based on legacy data and machine learning-technique: EthioSoilGrids 1.0 by Ali, Ashenafi, Tadesse, Solomon, Wolde, Yitbarek, Gudeta, Kiflu, Abera, Wuletawu, Mesfin, Ephrem, Mekete, Terefe, Haile, Mitiku, Haile, Wondwosen, Abegaz, Assefa, Tafesse, Demeke, Belay, Gebeyhu, Getahun, Mekonen, Beyene, Sheleme, Assen, Mohamed, Regassa, Alemayehu, Selassie, Yihenew G., Abebe, Dawit, Schulz, Steffen, Erkossa, Teklu, Hussien, Nesru, Yirdaw, Abebe, Mera, Addisu, Admas, Tesema, Wakoya, Feyera, Legesse, Awgachew, Tessema, Nigat, Abebe, Ayele, Gebremariam, Simret, Aregaw, Yismaw, Abebaw, Bizuayehu, Bekele, Damtew, Zewdie, Eylachew, Tamene, Lulseged D., Elias, Eyasu

“…Yet, a large legacy soil profile dataset accumulated over time and the emerging machine-learning modeling approaches can help in generating a high-quality quantitative digital soil map that can provide better soil information. …”
Get full text

Advancing non-optical water quality monitoring in Lake Tana, Ethiopia: insights from machine learning and remote sensing techniques by Leggesse, E. S., Zimale, F. A., Sultan, D., Enku, T., Tilahun, Seifu A.

Subjects: “…machine learning…”
Get full text

Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis by Ndour, Adama, Blasch, Gerald, Valente, João, Bisrat Gebrekidan, Sida, Tesfaye Shiferaw

“…This review focuses on the interplay of machine learning, UAV-based multispectral imagery and plant phenotyping. …”
Get full text

Assessing Sirex noctilio Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models by Hirigoyen, Andrés, Villacide, Jose Maria

“…This study investigates the potential of machine learning models and remote sensing at various spatiotemporal scales to assess forest damage caused by the woodwasp Sirex noctilio in pine plantations. …”
Get full text
Get full text
Get full text

LLS-SevEst – Late leaf spot severity estimator. A machine learning approach to assessing Nothopassalora personata in peanut by Herrador, Tiago A., Migotti Scaglia, Juliana, Paredes, Juan Andrés, Cazon, Luis Ignacio

Subjects: “…Machine Learning…”
Get full text
Get full text

Predicting Cattle Meat Types through Machine Learning Models Trained on Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data by Reyes L., Esther A.

Get full text

Monitoreo y gestión inteligente de cultivos : alertas preventivas y sistema de iluminación suplementario

Subjects: “…MACHINE LEARNING…”
Get full text

Monitoring spatial-temporal variations of surface areas of small reservoirs in Ghana’s Upper East Region using Sentinel-2 satellite imagery and machine learning by Ghansah, B., Foster, T., Higginbottom, T. P., Adhikari, R., Zwart, Sander J.

Subjects: “…machine learning…”
Get full text

Satellite-based tracking of agricultural adaptation progress by Reymondin, Louis, Golden, Aaron, Spillane, Charles

Subjects: “…machine learning…”
Get full text

Methylation in the CHH context allows to predict recombination in rice by Peñuela, Mauricio, Gallo-Franco, Jenny Johana, Finke, Jorge, Rocha, Camilo, Gkanogiannis, Anestis, Ghneim-Herrera, Thaura, Lorieux, Mathias

Subjects: “…machine learning…”
Get full text

Machine-supported decision-making to improve agricultural training participation and gender inclusivity by Reeves, Norman Peter, Ramadan, Ahmed, Sal y Rosas Celi, Victor Giancarlo, Medendorp, John William, Harun-Ar-Rashid, Krupnik, Timothy J., Lutomia, Anne Namatsi, Bello-Bravo, Julia, Pittendrigh, Barry Robert

Subjects: “…machine learning…”
Get full text

Toward sustainable land use planning: Land use mapping for Nandi County, Kenya by Hettiarachchi, Upeksha, Zhang, Wei, Zhe, Guo, Kibet, Walter

Subjects: “…machine learning…”
Get full text

Estimating gender inequalities in labor-market outcomes using mobile phone data by Seymour, Greg, Follett, Lendie, Henderson, Heath

Subjects: “…machine learning…”
Get full text