Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires

Romero, José R.; Roncallo, Pablo Federico; Akkiraju, Pavan C.; Ponzoni, Ignacio; Echenique, Viviana; Carballido, Jessica Andrea

doi:10.1016/j.compag.2013.05.006

Cited by 70 publications

(44 citation statements)

References 40 publications

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“…Prasad et al (2006) used piecewise linear regression method with breakpoint to predict corn and soybean yields based on remote sensing data and other surface parameters. Romero et al (2013) applied several machine learning methods such as decision tree and association rule mining for the classification of yield components of durum wheat and showed that association rule mining method obtained the best performance across all locations. This paper presents a deep learning framework that takes advantage of the state-of-the-art modeling and solution techniques to predict crop yield based on environmental data and management practices.…”

Section: Introductionmentioning

confidence: 99%

A CNN-RNN Framework for Crop Yield Prediction

2020

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Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper presents a deep learning framework using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for crop yield prediction based on environmental data and management practices. The proposed CNN-RNN model, along with other popular methods such as random forest (RF), deep fully connected neural networks (DFNN), and LASSO, was used to forecast corn and soybean yield across the entire Corn Belt (including 13 states) in the United States for years 2016, 2017, and 2018 using historical data. The new model achieved a root-mean-square-error (RMSE) 9% and 8% of their respective average yields, substantially outperforming all other methods that were tested. The CNN-RNN has three salient features that make it a potentially useful method for other crop yield prediction studies. (1) The CNN-RNN model was designed to capture the time dependencies of environmental factors and the genetic improvement of seeds over time without having their genotype information.(2) The model demonstrated the capability to generalize the yield prediction to untested environments without significant drop in the prediction accuracy. (3) Coupled with the backpropagation method, the model could reveal the extent to which weather conditions, accuracy of weather predictions, soil conditions, and management practices were able to explain the variation in the crop yields.

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Section: Introductionmentioning

confidence: 99%

A CNN-RNN Framework for Crop Yield Prediction

2020

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“…A CNN is a feed-forward neural network designed to process large-scale images by considering their local and global characteristics [69]. A neural network is typically comprised of multiple layers connected by a set of learnable weights and biases [70]. Convolutional layers represent a set of filters, each able to identify a particular feature in the image.…”

Section: Training and Validating The Convolutional Neural Network Modelmentioning

confidence: 99%

Using very high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

Duporge

Isupova

Reece

et al. 2020

Preprint

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Satellites allow large-scale surveys to be conducted in short time periods with repeat surveys possible <24hrs. Very high-resolution satellite imagery has been successfully used to detect and count a number of wildlife species in open, homogeneous landscapes and seascapes where target animals have a strong contrast with their environment. However, no research to date has detected animals in complex heterogeneous environments or detected elephants from space using very high-resolution satellite imagery and deep learning.In this study we apply a Convolution Neural Network (CNN) model to automatically detect and count African elephants in a woodland savanna ecosystem in South Africa. We use WorldView-3 and 4 satellite data – the highest resolution satellite imagery commercially available. We train and test the model on eleven images from 2014-2019. We compare the performance accuracy of the CNN against human accuracy. Additionally, we apply the model on a coarser resolution satellite image (GeoEye-1) captured in Kenya to test if the algorithm can generalise to an elephant population outside of the training area.Our results show the CNN performs with high accuracy, comparable to human detection capabilities. The detection accuracy (i.e., F2 score) of the CNN models was 0.78 in heterogeneous areas and 0.73 in homogenous areas. This compares with the detection accuracy of the human labels with an averaged F2 score 0.77 in heterogeneous areas and 0.80 in homogenous areas. The CNN model can generalise to detect elephants in a different geographical location and from a lower resolution satellite.Our study demonstrates the feasibility of applying state-of-the-art satellite remote sensing and deep learning technologies for detecting and counting African elephants in heterogeneous landscapes. The study showcases the feasibility of using high resolution satellite imagery as a promising new wildlife surveying technique. Through creation of a customised training dataset and application of a Convolutional Neural Network, we have automated the detection of elephants in satellite imagery with as high accuracy as human detection capabilities. The success of the model to detect elephants outside of the training data site demonstrates the generalisability of the technique.

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“…Pantazi, Xanthoula Eirini, et al [12] an. Romero, José R., et al [13] have explained how machine learning techniques are used in guessing the yield of wheat crops. X.E.…”

Section: Automatic Crop Yield Predictionmentioning

confidence: 99%

Study on Machine Learning for Identification of Farmer’s Query in Kannada Language

Pai¹,

Of²,

Hegde³

2019

IJCA

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Agriculture is considered as the economic backbone of almost all the countries in the world. As there is increase in population, the need for basic requirements such as food, clothing, shelters, and medicines also increases. To meet all these needs, advancement in agriculture is needed. Various technologies have been introduced in agriculture to increase the yield of several crops. One among them is the machine learning technology. Machine learning is an important area in computer science that allows the computers to write programs on its own without being explicitly programmed. Machine learning techniques in agriculture help to improve the traditional farming techniques in a very efficient way. The main purpose of this paper is to learn different use of machine learning techniques in agriculture and to develop machine learning model for agriculture.

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Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires

Cited by 70 publications

References 40 publications

A CNN-RNN Framework for Crop Yield Prediction

A CNN-RNN Framework for Crop Yield Prediction

Using very high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

Study on Machine Learning for Identification of Farmer’s Query in Kannada Language

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