Nonlinear parametric modelling to study how soil properties affect crop yields and NDVI

Whetton, Rebecca; Zhao, Yifan; Shaddad, Sameh M.; Mouazen, Abdul Mounem

doi:10.1016/j.compag.2017.04.016

Cited by 40 publications

(13 citation statements)

References 40 publications

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“…Linear, trend, and correlation analyses methods were mostly used to qualitatively analyze the spatio-temporal changes in vegetation cover in previous studies [34][35][36], while the geographical detector model can detect numerical and qualitative data and effectively identify spatial differentiation of vegetation [37,38]. As a powerful tool for driving force and factor analyses, a geographical detector is able to quantify the driving force and the influence of its interaction in a robust and direct manner, and does not have to strictly follow the assumptions of traditional statistical methods [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Changes and Driving Forces of Vegetation Coverage on the Loess Plateau of Northern Shaanxi

et al. 2021

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As an important indicator of terrestrial ecosystems, vegetation plays an important role in the study of global or regional ecological environmental changes. Northern Shaanxi is located in the ecologically fragile area of the Loess Plateau, which is affected by interactions between natural and human factors. Here, we used the Normalized Difference Vegetation Index (NDVI) as an indicator to study the temporal and spatial variations of vegetation in Northern Shaanxi from 2000 to 2018. Based on the geographic detector method which can detect spatial differentiation, we analyzed the spatial differentiation characteristics and driving forces of vegetation in Northern Shaanxi, and revealed the most appropriate range or type of influencing factors for promoting vegetation growth. The results showed that the overall vegetation coverage improved in the study area, and NDVI showed an increasing trend with a growth rate of 0.10/10 years from 2000 to 2018. Natural and human factors are crucial driving forces of NDVI change, among which gross domestic product, land-use type, slope, and temperature have the greatest influence. The interaction between natural and human factors on NDVI was dominated by nonlinear and mutual enhancement effects, and the influence of interactions among all factors was significantly higher than that of a single factor. The range or types of factors suitable for vegetation growth were analyzed in the study area, and the joint action of natural and human factors had a more significant impact on vegetation. These findings provide a scientific basis for local governments to intervene in vegetation changes and ecological restoration through natural and human factors within the favorable scope.

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Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Changes and Driving Forces of Vegetation Coverage on the Loess Plateau of Northern Shaanxi

et al. 2021

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“…The crop yield processes and strategies vary with time and they are profoundly non-linear in nature [6], and intricate due to the integration of a wide extent of correlated factors [7], [8] characterized and impacted by non-arbitrate runs and external aspects. Usually, a considerable part of the agricultural framework cannot be delineated in a fundamental stepwise calculation, especially with complex, incomplete, ambiguous and strident datasets.…”

Section: Introductionmentioning

confidence: 99%

Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications

2020

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Predicting crop yield based on the environmental, soil, water and crop parameters has been a potential research topic. Deep-learning-based models are broadly used to extract significant crop features for prediction. Though these methods could resolve the yield prediction problem there exist the following inadequacies: Unable to create a direct non-linear or linear mapping between the raw data and crop yield values; and the performance of those models highly relies on the quality of the extracted features. Deep reinforcement learning provides direction and motivation for the aforementioned shortcomings. Combining the intelligence of reinforcement learning and deep learning, deep reinforcement learning builds a complete crop yield prediction framework that can map the raw data to the crop prediction values. The proposed work constructs a Deep Recurrent Q-Network model which is a Recurrent Neural Network deep learning algorithm over the Q-Learning reinforcement learning algorithm to forecast the crop yield. The sequentially stacked layers of Recurrent Neural network is fed by the data parameters. The Q-learning network constructs a crop yield prediction environment based on the input parameters. A linear layer maps the Recurrent Neural Network output values to the Q-values. The reinforcement learning agent incorporates a combination of parametric features with the threshold that assist in predicting crop yield. Finally, the agent receives an aggregate score for the actions performed by minimizing the error and maximizing the forecast accuracy. The proposed model efficiently predicts the crop yield outperforming existing models by preserving the original data distribution with an accuracy of 93.7%.INDEX TERMS Crop yield prediction, deep recurrent Q-network, deep reinforcement learning, intelligent agrarian application.

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“…It overcomes the low efficiency problems associated with local downloads, storage, and preprocessing and uses Google's powerful computing capabilities to analyze and process a variety of environmental and social data [16]. With these advantages, GEE has been widely used to map land cover types and associated changes over large areas [41], perform data fusion [42,43], act as a geodetector [44,45], and investigate ecological environments [46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Spatial pattern of the ecological environment in Yunnan Province

Wang

Ding

2021

PLoS ONE

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Ecological science focuses on the structure and function of the natural environment. However, the study of ecological environments primarily focuses on single-element research and lacks a comprehensive perspective. To examine ecological environmental trends on different scales, the present paper selected Yunnan Province as the study area. Chemical oxygen demand, rocky desertification, forest coverage, natural disaster data and spatial analysis methods were used to obtain the ecological environmental characteristics of each county and construct a comprehensive evaluation method of the ecological environment. The present paper revealed that the environmental capacity in Yunnan Province was at a moderate level, the ecological environment fragility was remarkable, the significance of the ecological environment was very high, natural disasters occurred frequently, and spatial differentiation between ecological environments was obvious. The province may be divided into three functional areas: the comprehensive-balanced area, the efficiency-dominated area and the environment-dominated area. Central Yunnan was a key development zone and the main area for the manufacturing and service industries, which were built as a modern industrial system in Yunnan Province. The ecological environment in northwestern Yunnan and southern Yunnan is of high significance, and this region was an ecological environment protection area that was important area for the construction of the modern agricultural system in Yunnan Province. To achieve sustainable development of the ecological environment, the spatial characteristics of the ecological environment must be determined at the county scale.

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Nonlinear parametric modelling to study how soil properties affect crop yields and NDVI

Cited by 40 publications

References 40 publications

Spatio-Temporal Changes and Driving Forces of Vegetation Coverage on the Loess Plateau of Northern Shaanxi

Spatio-Temporal Changes and Driving Forces of Vegetation Coverage on the Loess Plateau of Northern Shaanxi

Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications

Spatial pattern of the ecological environment in Yunnan Province

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