Prediction of winter wheat yield and dry matter in North China Plain using machine learning algorithms for optimal water and nitrogen application

Wang, Ying; Shi, Wen‐Juan; Wen, Tianyang

doi:10.1016/j.agwat.2023.108140

Cited by 34 publications

(5 citation statements)

References 59 publications

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“…For the machine learning models, the following methods were used: linear regression, which is a statistical method used to determine relationships between a dependent variable (value to be predicted) and one or more independent variables (predictors), assuming a linear relationship between the variables [51]; neural networks (ANNs), which consist of a simplified process inspired by biological neural networks, enabling pattern recognition in complex datasets through learning [52,53]; support vector machines (SVMs), with learning that involves pattern recognition by defining a hyperplane that partitions the data into homogeneous areas, to assist in the prediction processes [25,54]; cubist (Cub), a model tree method characterized by the use of trees for further regression analysis, where regression is applied on the subset of data that are formed after the data partitioning performed by the trees [55]; boosted regression trees (BRTs), notable for their high predictive power when considering the concept of recursive binary splitting in conjunction with a learning (boosting) technique [56]; and classification and regression trees (CARTs), based on rules that allow trees to be created from recursive partitioning, dividing data into subsets based on independent factors [57].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Can Unmanned Aerial Vehicle Images Be Used to Estimate Forage Production Parameters in Agroforestry Systems in the Caatinga?

Santos,

Costa,

Medeiros

et al. 2024

Applied Sciences

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The environmental changes in the Caatinga biome have already resulted in it reaching levels of approximately 50% of its original vegetation, making it the third most degraded biome in Brazil, due to inadequate grazing practices that are driven by the difficulty of monitoring and estimating the yield parameters of forage plants, especially in agroforestry systems (AFS) in this biome. This study aimed to compare the predictive ability of different indexes with regard to the biomass and leaf area index of forage crops (bushveld signal grass and buffel grass) in AFS in the Caatinga biome and to evaluate the influence of removing system components on model performance. The normalized green red difference index (NGRDI) and the visible atmospherically resistant index (VARI) showed higher correlations (p < 0.05) with the variables. In addition, removing trees from the orthomosaics was the approach that most favored the correlation values. The models based on classification and regression trees (CARTs) showed lower RMSE values, presenting values of 3020.86, 1201.75, and 0.20 for FB, DB, and LAI, respectively, as well as higher CCC values (0.94). Using NGRDI and VARI, removing trees from the images, and using CART are recommended in estimating biomass and leaf area index in agroforestry systems in the Caatinga biome.

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Section: Machine Learning Modelsmentioning

confidence: 99%

Can Unmanned Aerial Vehicle Images Be Used to Estimate Forage Production Parameters in Agroforestry Systems in the Caatinga?

Santos,

Costa,

Medeiros

et al. 2024

Applied Sciences

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“…A decision tree (DT) classifies data by a set of rules [31]. It provides a rule-like approach to which values will be obtained under which conditions.…”

Section: Decision Treementioning

confidence: 99%

Maize Nitrogen Grading Estimation Method Based on UAV Images and an Improved Shufflenet Network

Sun

Zhang

2023

Agronomy

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Maize is a vital crop in China for both food and industry. The nitrogen content plays a crucial role in its growth and yield. Previous researchers have conducted numerous studies on the issue of the nitrogen content in single maize plants from a regression perspective; however, partition management techniques of precision agriculture require plants to be divided by zones and classes. Therefore, in this study, the focus is shifted to the problems of plot classification and graded nitrogen estimation in maize plots performed based on various machine learning and deep learning methods. Firstly, the panoramic unmanned aerial vehicle (UAV) images of maize farmland are collected by UAV and preprocessed to obtain UAV images of each maize plot to construct the required datasets. The dataset includes three classes—low nitrogen, medium nitrogen, and high nitrogen, with 154, 94, and 46 sets of UAV images, respectively, in each class. The training set accounts for eighty percent of the entire dataset and the test set accounts for the other twenty percent. Then, the dataset is used to train models based on machine learning and convolutional neural network algorithms and subsequently the models are evaluated. Comparisons are made between five machine learning classifiers and four convolutional neural networks to assess their respective performances, followed by a separate assessment of the most optimal machine learning classifier and convolutional neural networks. Finally, the ShuffleNet network is enhanced by incorporating SENet and improving the kernel size of the Depthwise separable convolution. The findings demonstrate that the enhanced ShuffleNet network has the highest performance; its classification accuracy, precision, recall, and F1 scores were 96.8%, 97.0%, 97.1%, and 97.0%, respectively. The RegNet, the optimal model among deep learning models, achieved accuracy, precision, recall, and F1 scores of 96.4%, 96.9%, 96.5%, and 96.6%, respectively. In comparison, logistic regression, the optimal model among the machine learning classifiers, attained accuracy of 77.6%, precision of 79.5%, recall of 77.6%, and an F1 score of 72.6%. Notably, the logistic regression exhibited significant enhancements of 19.2% in accuracy, 17.5% in precision, 19.5% in recall, and 24.4% in the F1 score. In contrast, RegNet demonstrated modest improvements of 0.4% in accuracy, 0.1% in precision, 0.6% in recall, and 0.4% in the F1 score. Moreover, ShuffleNet-improvement boasted a substantially lower loss rate of 0.117, which was 0.039 lower than that of RegNet (0.156). The results indicated the significance of ShuffleNet-improvement in the nitrogen classification of maize plots, providing strong support for agricultural zoning management and precise fertilization.

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“…Based on the previous 12 years’ data, researchers proposed an ML-based crop yield prediction in North China Plan. To find the best model, they investigate several ML algorithms on winter wheat and dry matter prediction ( Wang et al., 2023 ).…”

Section: Related Workmentioning

confidence: 99%

Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation

et al. 2023

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Agriculture is the most critical sector for food supply on the earth, and it is also responsible for supplying raw materials for other industrial productions. Currently, the growth in agricultural production is not sufficient to keep up with the growing population, which may result in a food shortfall for the world’s inhabitants. As a result, increasing food production is crucial for developing nations with limited land and resources. It is essential to select a suitable crop for a specific region to increase its production rate. Effective crop production forecasting in that area based on historical data, including environmental and cultivation areas, and crop production amount, is required. However, the data for such forecasting are not publicly available. As such, in this paper, we take a case study of a developing country, Bangladesh, whose economy relies on agriculture. We first gather and preprocess the data from the relevant research institutions of Bangladesh and then propose an ensemble machine learning approach, called K-nearest Neighbor Random Forest Ridge Regression (KRR), to effectively predict the production of the major crops (three different kinds of rice, potato, and wheat). KRR is designed after investigating five existing traditional machine learning (Support Vector Regression, Naïve Bayes, and Ridge Regression) and ensemble learning (Random Forest and CatBoost) algorithms. We consider four classical evaluation metrics, i.e., mean absolute error, mean square error (MSE), root MSE, and R2, to evaluate the performance of the proposed KRR over the other machine learning models. It shows 0.009 MSE, 99% R2 for Aus; 0.92 MSE, 90% R2 for Aman; 0.246 MSE, 99% R2 for Boro; 0.062 MSE, 99% R2 for wheat; and 0.016 MSE, 99% R2 for potato production prediction. The Diebold–Mariano test is conducted to check the robustness of the proposed ensemble model, KRR. In most cases, it shows 1% and 5% significance compared to the benchmark ML models. Lastly, we design a recommender system that suggests suitable crops for a specific land area for cultivation in the next season. We believe that the proposed paradigm will help the farmers and personnel in the agricultural sector leverage proper crop cultivation and production.

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Prediction of winter wheat yield and dry matter in North China Plain using machine learning algorithms for optimal water and nitrogen application

Cited by 34 publications

References 59 publications

Can Unmanned Aerial Vehicle Images Be Used to Estimate Forage Production Parameters in Agroforestry Systems in the Caatinga?

Can Unmanned Aerial Vehicle Images Be Used to Estimate Forage Production Parameters in Agroforestry Systems in the Caatinga?

Maize Nitrogen Grading Estimation Method Based on UAV Images and an Improved Shufflenet Network

Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation

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