Crop yield prediction with deep convolutional neural networks

By Petteri Nevavuori, Nathaniel G. Narra, T. Lipping. Computers and Electronics in Agriculture, 2019

486 citations recorded by the source corpus.

Abstract Using remote sensing and UAVs in smart farming is gaining momentum worldwide. The main objectives are crop and weed detection, biomass evaluation and yield prediction. Evaluating machine learning methods for remote sensing based yield prediction requires availability of yield mapping devices, which are still not very common among farmers. In this study Convolutional Neural Networks (CNNs) – a deep learning methodology showing outstanding performance in image classification tasks – are applied to build a model for crop yield prediction based on NDVI and RGB data acquired from UAVs. The effect of various aspects of the CNN such as selection of the training algorithm, depth of the network, regularization strategy, and tuning of the hyperparameters on the prediction efficiency are tested. Using the Adadelta training algorithm, L 2 regularization with early stopping and a CNN with 6 convolutional layers, mean absolute error (MAE) in yield prediction of 484.3 kg/ha and mean absolute percentage error (MAPE) of 8.8% was achieved for data acquired during the early period of the growth season (i.e., in June of 2017, growth phase 25%), MAE of 624.3 kg/ha (MAPE: 12.6%) was obtained. Significantly, the CNN architecture performed better with RGB data than the NDVI data.

Subfields: Ecology, Plant Science, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/8063153ca523ca72b6254f5bdd083cd9aaf0559c

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Citations in the corpus

Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “[117] Wheat, barley Multispectral images from UAV Prediction of barley and wheat yields CNN (1) Early growth phase(<25%): MAE = 484.”
Machine Learning Applications for Precision Agriculture: A Comprehensive Review (2021, background): “Nevavuori et al. [57] explored a deep learning approach, i.e. CNN for wheat and barley yield prediction in the agriculture ﬁeld of Pori, Finland.”
County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model (2019, methodology): “proposed a CNN model for crop yield prediction based on NDVI and RGB data acquired from UAVs, the result showed the CNN architecture performed better with RGB data than the NDVI data [23].”
Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review (2020, unknown): “Finally, a deep convolutional neural network was developed for the prediction of crop yield in [102] .”
Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications (2020, background): “Crop yield prediction can be considered as a pattern recognition problem where AI has shown notable efﬁciency for agricultural applications [38].”