Search Results - "Truth."

A simple algorithm outperforms a machine learning approach for quantifying spittlebug damage in tropical grasses by Ruiz-Hurtado, Andres Felipe, Espitia, Paula, Cardoso, Juan Andres, Jauregui, Rosa Noemi

“…Considering the large data volumes in breeding trials, where five replicates of ~150 genotypes are assessed to spittlebug damage, often with limited availability of ground truth data, unsupervised learning approaches like clustering are preferred for damage segmentation. …”
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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

“…We collected 470 ground-truth N measurements and captured corresponding RGB images with a drone-mounted camera. …”
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A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF) by Kira, Oz, Wen, Jiaming, Han, Jimei, McDonald, Andrew J., Barrett, Christopher B., Ortiz-Bobea, Ariel, Liu, Yanyan, You, Liangzhi, Mueller, Nathaniel D., Sun, Ying

“…These results demonstrate the potential advantage of MLR-SIF for yield estimation in developing countries where ground truth data is limited in quantity and quality. In addition, high-resolution and crop-specific satellite SIF is crucial for accurate yield estimation. …”
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A critical synthesis of remote sensing and machine learning approaches for climate hazard impact on crop yield by Obahoundje, Salomon, Tilahun, Seifu A., Zemadim, Birhanu, Schmitter, Petra

“…The research gaps include a limited investigation at the sub-national level, insufficient ground-truth validation, and inadequate monitoring of complex, compounding hazards like drought–flood–heatwave interactions. …”
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Remote sensing and GIS modeling for selection of a benchmark research area in the inland valley agroecosytems of West and Central Africa by Thenkabail, Prasad S., Nolte, C., Lyon, J.

“…The focus here is a methodology for Level II characterization for benchmark research-area selection using SPOT HRV data, secondary GIS datasets, and detailed ground-truth data with GPs locations. The spatial datalayers were analyzed in a GIS modeling framework. …”
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Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images by Atanbori, John, Montoya Pizarro, Maria Elker, Selvaraj, Michael Gomez, French, Andrew P., Pridmore, Tony P.

“…We automatically form cassava storage root masks for any missing classes using existing ground-truth masks, and input them as a condition to our GAN model to generate synthetic root images. …”
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Genomic resources to guide improvement of the shea tree by Hale, I., Ma, X., Melo, A.T.O., Padi, Francis K, Hendre, P.S., Kingan, S.B., Sullivan, S.T., Chen, S., Boffa, J.-M., Muchugi, Alice, Danquah, A., Barnor, M.T., Jamnadass, Ramni H., Peer, Y. van de, Deynze, A. van

“…Despite its economic and cultural value, however, not to mention the ecological roles it plays as a dominant parkland species, shea remains semi-domesticated with virtually no history of systematic genetic improvement. In truth, shea’s extended juvenile period makes traditional breeding approaches untenable; but the opportunity for genome-assisted breeding is immense, provided the foundational resources are available. …”
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Using machine learning for image-based analysis of sweetpotato root sensory attributes by Nakatumba-Nabende, J., Babirye, C., Tusubira, J., Mutegeki, H., Nabiryo, A., Murindanyi, S., Katumba, A., Nantongo, J.S., Sserunkuma, E., Nakitto, M., Ssali, R.T., Makunde, G.S., Moyo, M., Campos, Hugo

“…For flesh-colour the trained Linear Regression and Random Forest Regression models attained 𝑅2 values of 0.92 and 0.87, respectively, against the ground truth values given by a human sensory panel. In contrast, the Random Forest Regressor and Gradient Boosting model attained 𝑅2 values of 0.85 and 0.80, respectively, for the prediction of mealiness. …”
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Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data by Gumma, Murali Krishna, Thenkabail, Prasad S., Hideto, Fujii, Nelson, Andrew, Dheeravath, Venkateswarlu, Busia, Dawuni, Rala, Arnel

“…Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. …”
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Rice area-yield estimation based on the synergistic use of remote sensing time-series and crop growth modeling in Nigeria by Barbieri, Massimo, Quicho, Emma D., Ibrahim, Ali, Melchiori, Luca, Cattaneo, Alessandro, Gatti, Luca, Copa, Loris, Holecz, Francesco, Mathieu, Renaud, Saito, Kazuki, Senthilkumar, Kalimuthu

“…Methods Over 1500 geolocated ground-truth points were collected across Kano, Jigawa, and Benue States during the 2022 and 2023 wet and dry seasons. …”
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Advancing Soil Moisture Prediction Using Satellite and UAV-based Imagery with Machine Learning Models by Hussain, S., Arshad, M., Cheema, Muhammad Jehanzeb Masud, Qamar, M. U., Wajid, S. A., Daccache, A.

“…Satellite data and UAV imagery were processed to calculate soil moisture indices, including the Normalized Difference Water Index (NDWI), Moisture Vegetation Index (MVI), Water Stress Index (WSI), and Drought Stress Water Index (DSWI-4). Ground-truth data, including in situ soil moisture measurements at 15 cm depth and crop yield observations, were recorded for validation. …”
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Year-End Newsletter of the CGIAR Science Program on Food Frontiers and Security by Awinoh, Martha, Okello, Anna, Hollerich, Gillian, Carrillo, Lucia, Ibukun, Taiwo, Victor, Andin, Hanke-Louw, Nora

“…Yet within these challenges, one truth is clear: food systems on the frontlines must become stronger, safer, and more inclusive. …”
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Land use and land cover change, drivers and its impact: A comparative study from Kuhar Michael and Lenche Dima of Blue Nile and Awash Basins of Ethiopia by Ali, H.

“…Socio-economic Survey and review of documents was carried out to understand historical trends, collect ground truth and other secondary information required. Analysis of data and other data was accomplished through integrated use of ERDAS imagine (version 9.1), ENVI (version 4.3) and ArcGIS (version 9.2) software packages along with Microsoft office analytical tools. …”
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Äldre och få, om landsbygdskommunernas befolkning by Eriksson, Daniel, Kuortti, Hannes

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Life history trade-offs in Anadromous Burbot Lota lota (Linnaeus 1758) from Rickleån and Sävarån, Northern Sweden by Sandberg, Mikael

“…Whenever a new discovery is reported to the scientific world, they say first, “It is probably not true.” Thereafter, when the truth of the new proposition has been demonstrated beyond question, they say, “Yes, it may be true, but it is not important.” …”

Den tillfälliga Ångermanälven by Wright, Hannah

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Evaluating inventory methods for estimating stem diameter distributions in micro stands derived from airborn laser scanning by Lundholm, Anders

“…The three sets of imputed trees were compared to the measured variables (ground truth) on the validation plots to assess the accuracy. …”

Efecto de la participacion en el desarrollo local sobre la identidad de las organizaciones participantes by Piemontese, Elena Beatriz

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Land cover changes in the Upper Great Ruaha (Tanzania) and the Upper Awash (Ethiopia) river basins and their potential implications for groundwater resources by Chandrasekharan, Kiran M., Villholth, Karen G., Kashaigili, J. J., Gebregziabher, Gebrehaweria, Mandela, P. J.

“…The spatio-temporal analysis spans a recent 15-20-year period up until 2015/16 and utilizes remote sensing imagery, secondary maps and ground truth information for the two end point times (resolution: 30 m). …”
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Accelerating technical change through ICTs: Evidence from a video-mediated extension experiment in Ethiopia by Abate, Gashaw T., Bernard, Tanguy, Makhija, Simrin, Spielman, David J.

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