YieldNet: A Convolutional Neural Network for Simultaneous Corn and Soybean Yield Prediction Based on Remote Sensing Data

Khaki, Saeed; Pham, Hieu; Wang, Lizhi

doi:10.1101/2020.12.05.413203

Cited by 12 publications

(10 citation statements)

References 33 publications

(32 reference statements)

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“…It is mainly divided into encoding and decoding structure and expansion of convolution structure; The network representatives of encoding and decoding structure include U-net [ 18 ], Seg Net [ 19 ], Refinet [ 20 ], etc., where an encoder is used to extract image features and dimension reduction, and a decoder is used to recover image dimension and spatial information. The representative networks of expansive convolution are Deep Labv1 [ 21 ], V2 [ 22 ], V3 [ 23 ], V3+ [ 24 ],and PSPNet [ 25 ] which can increase the size of the input image even if no pooling layer is used so that each convolution can contain more information when outputting. In addition, the networks with good effect in the field of target detection have also been applied to the field of instance segmentation, and achieved good segmentation results, such as regional convolution network (R–CNN) [ 26 ], FAST R–CNN [ 27 ], Faster R–CNN [ 28 ], Maskr-CNN [ 29 ], and so on.…”

Section: Deep Learning Algorithm and Its Application In Crop Yield Pr...mentioning

confidence: 99%

Research on Rice Yield Prediction Model Based on Deep Learning

Han

Liu

He³

et al. 2022

Computational Intelligence and Neuroscience

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Food is the paramount necessity of the people. With the progress of society and the improvement of social welfare system, the living standards of people all over the world are constantly improving. The development of medical industry improves people’s health level constantly, and the world population is constantly climbing to a new peak. With the continuous development of deep learning in recent years, its advantages are constantly displayed, especially in the aspect of image recognition and processing, it drives into the distance. Thanks to the superiority of deep learning in image processing, the combination of remote sensing images and deep learning has attracted more attention. To simulate the four key factors of rice yield, this article tries a regression model with a combination of various characteristic independent variables. In this article, the selection of the best linear and nonlinear regression models is discussed, the prediction performance and significance of each regression model are analyzed, and some thoughts are given on estimation of actual rice yield.

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Section: Deep Learning Algorithm and Its Application In Crop Yield Pr...mentioning

confidence: 99%

Research on Rice Yield Prediction Model Based on Deep Learning

Han

Liu

He³

et al. 2022

Computational Intelligence and Neuroscience

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“…Constraint (8) means that each population can only be harvested in one week. Constraints (9) and (10) enforce the model to harvest populations as soon as they accumulate their required GDU. Constraint (11) requires that w j = 1 when any population is harvested in week j.…”

Section: Optimization Modelsmentioning

confidence: 99%

Corn planting and harvest scheduling under storage capacity and growing degree units uncertainty

Khalilzadeh

Wang

2022

Sci Rep

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Planting and harvest scheduling is a crucial part of crop production due to its significant impact on other factors such as balancing the capacities for harvest, yield potential, sales price, storage, and transportation. Corn planting and harvest scheduling is challenging because corn hybrids have different planting windows, and, subsequently, inaccurate planting and harvest scheduling can result in inconsistent and unpredictable weekly harvest quantities and logistical and productivity issues. In the 2021 Syngenta Crop Challenge, participants were given several large datasets including recorded historical daily growing degree units (GDU) of two sites and provided with planting windows, required GDUs, and harvest quantities of corn hybrids planted in these two sites, and were asked to schedule planting and harvesting dates of corn hybrids under two storage capacity cases so that facilities are not over capacity in harvesting weeks and have consistent weekly harvest quantities. The research problem includes determining the planting and harvest scheduling of corn hybrids under two storage capacity cases: (1) given the maximum storage capacity, and (2) without maximum storage capacity to determine the lowest storage capacity for each site. To help improve corn planting and harvest scheduling, we propose two mixed-integer linear programming (MILP) models and a heuristic algorithm to solve this problem for both storage capacity cases. Daily GDUs are required for planting and harvest scheduling, but they are unknown at the beginning of the growing season. As such, we use recurrent neural networks to predict the weekly GDUs of 70 weeks and consider this as the predicted GDU scenario to solve this problem. In addition, we solve this problem considering all given 10 historical GDU scenarios from 2010 to 2019 together for both storage capacity cases to include historical GDUs directly to our model rather than using predicted GDUs. Our extensive computational experiments and results demonstrate the effectiveness of our proposed methods, which can provide optimal planting and harvest scheduling considering deterministic GDU scenario and uncertainties in historical GDU scenarios for both storage capacity cases to provide consistent weekly harvest quantities that are below the maximum capacity.

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“…DNN models are trained with gradient-based optimization methods to minimize the desired error function for the task for which they are used. DNN models have recently been used for crop yield prediction which has shown great success by outperforming other traditional machine learning methods 13,16,[36][37][38] The traditional neural networks with a single hidden layer have also been widely used to estimate FC and PWP 39,40 . This method is a mathematical model developed by the inspiration of the structure of the biological brain 41 .…”

Section: Deep Neural Network (Dnn)mentioning

confidence: 99%

Winter Wheat Yield Prediction Using Convolutional Neural Networks from Environmental and Phenological Data

Srivastava¹,

Safaei²,

Khaki³

et al. 2021

Preprint

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Crop yield forecasting depends on many interactive factors including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using extensive datasets of weather, soil, and crop phenology. We propose a convolutional neural network (CNN) which uses the 1-dimentional convolution operation to capture the time dependencies of environmental variables. The proposed CNN, evaluated along with other machine learning models for winter wheat yield prediction in Germany, outperformed all other models tested. To address the seasonality, weekly features were used that explicitly take soil moisture and meteorological events into account. Our results indicated that nonlinear models such as deep learning models and XGboost are more effective in finding the functional relationship between the crop yield and input data compared to linear models and deep neural networks had a higher prediction accuracy than XGboost. One of the main limitations of machine learning models is their black box property. Therefore, we moved beyond prediction and performed feature selection, as it provides key results towards explaining yield prediction (variable importance by time). As such, our study indicates which variables have the most significant effect on winter wheat yield.

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YieldNet: A Convolutional Neural Network for Simultaneous Corn and Soybean Yield Prediction Based on Remote Sensing Data

Cited by 12 publications

References 33 publications

Research on Rice Yield Prediction Model Based on Deep Learning

Research on Rice Yield Prediction Model Based on Deep Learning

Corn planting and harvest scheduling under storage capacity and growing degree units uncertainty

Winter Wheat Yield Prediction Using Convolutional Neural Networks from Environmental and Phenological Data

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