Работы, цитирующие эту работу

Работ: 5

Работа: Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques

1. Sunflower crop yield prediction by advanced statistical modeling using satellite-derived vegetation indices and crop phenology

   Khilola Amankulova, Nizom Farmonov, Uzbekkhon Mukhtorov +1

   СтатьяRemote Sensing in AgricultureGeocarto International2023Цитирований: 7

   ABIABI:AkademIndex/openalex/2023.article.000152

2. Effectiveness of machine learning and deep learning models at county-level soybean yield forecasting

   Nizom Farmonov, Khilola Amankulova, Shahid Nawaz Khan +6

   СтатьяRemote Sensing in AgricultureHungarian Geographical Bulletin2024Цитирований: 1

   ABIABI:AkademIndex/openalex/2024.article.001464

3. Integrating the Sentinel-1, Sentinel-2 and topographic data into soybean yield modelling using machine learning

   Khilola Amankulova, Nizom Farmonov, Khasan Omonov +2

   СтатьяRemote Sensing in AgricultureAdvances in Space Research2024Цитирований: 0

   ABIABI:AkademIndex/openalex/2024.article.000252

4. A Novel Fusion Method for Soybean Yield Prediction Using Sentinel-2 and PlanetScope Imagery

   Khilola Amankulova, Nizom Farmonov, Enas Abdelsamei +7

   СтатьяRemote Sensing in AgricultureIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2024Цитирований: 0

   ABIABI:AkademIndex/openalex/2024.article.000464

5. Spatio-Temporal Assessment of Land Cover Change and Cropping Pattern Variation Using Cloud-Based Earth Observation Analytics

   Lal Mohammad, Jatisankar Bandyopadhyay, Ismail Mondal +3

   СтатьяRemote Sensing in AgricultureJournal of the Indian Society of Remote Sensing2026Цитирований: 0

   ABIABI:AkademIndex/openalex/2026.article.010553