Estimating crop yields with deep learning and remotely sensed data

By Kentaro Kuwata, Ryosuke Shibasaki. 2015

208 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Plant Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

In Ecology

In Plant Science

Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “DL also has been used in remote sensing for land and crop detection and classification [109] [110] [111].”
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data (2017, methodology): “Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Long-short Term Memory (LSTM) are some typical architectures (LeCun, Bengio, and Hinton 2015).”
A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, methodology): “Besides, CNNs are successfully used in the field of agriculture for recognition of plant species [63], yield management [94,95], weed detection [96], soil and water management [97], fruit counting [98], diseases and pest detection [82,99], evaluating the nutrient status of plants [100], and monitori”
Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture (2020, background): “Existing studies are, for example, investigating the estimation of crop yield using CNN and multispectral images together with climate data [197], plant disease detection using CNN and smartphone-acquired images [198], crop classification using 3-D CNN and multi-temporal multispectral images [199], ”
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “In global change analysis, DL models show advantages in estimating crop yield (Kuwata & Shibasaki, 2015; You et al., 2017).”
Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data (2019, background): “…of applications such as mapping land-cover (Li et al. 2016) and crops (Kussul et al. 2017; Zhong, Lina, and Zhou 2019), estimating crop yields (Kuwata and Shibasaki 2015), detecting oil palm trees (Li et al. 2017) and plant diseases (Mohanty, Hughes, and Marcel 2016) with accuracies often…”
A review of the use of convolutional neural networks in agriculture (2018, methodology): “…(Christiansen et al ., 2016; Steen et al ., 2016) and fruit counting (Sa et al ., 2016; Rahnemoonfar and Sheppard, 2017), while some other papers focused on predicting future values such as maize yield (Kuwata and Shibasaki, 2015) and soil moisture content in the field (Song et al ., 2016).”
Deep learning in environmental remote sensing: Achievements and challenges (2020, methodology): “Kuwata and Shibasaki (2015) used a DL model of two inner product layers to simulate the relationship between meteorological parameters of MODIS EVI and crop yield.”
Machine Learning Applications for Precision Agriculture: A Comprehensive Review (2021, methodology): “Kuwata and Shibasaki [51] employed DL models to estimate the crop yield.”
County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model (2019, methodology): “Kuwata and Shibasaki used a Caffe-based deep learning regression model (Gaussian Radial Basis Function) trained with remotely sensed data (satellite data, climate data, and environmental metadata) to model corn crop yield estimation at county-level [22].”
Deep learning in agriculture: A survey (2018, methodology): “CNN had also superior performance than Penalized Discriminant Analysis (Grinblat, Uzal, Larese, & Granitto, 2016), SVM Regression (Kuwata & Shibasaki, 2015), area-based techniques (Rahnemoonfar & Sheppard, 2017), texturebased regression models (Chen, et al., 2017), LMC classifiers (Xinshao & Cheng, ”