Soil monitoring for precision farming using hyperspectral remote sensing and soil sensors

Schreiner, Simon; Ćulibrk, Dubravko; Bandecchi, Michele; Gross, Wolfgang L.; Middelmann, Wolfgang

doi:10.1515/auto-2020-0042

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…Because adjacent pixels in an image usually have similar features, the adjacent pixels are similar by convolving the output, resulting in redundant information. Grouping layer aims to solve this problem by reducing the output size, reducing the number of exported values in the grouping layer, and retaining useful information [10][11].…”

Section: Hierarchical Structure Of Deep Cnnmentioning

confidence: 99%

Deep Convolution Neural Network in Remote Sensing Monitoring of Water Source Pollution Sources

2022

WPPCP

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In recent years, the eutrophication of Lake Reservoir type drinking water source areas has become increasingly prominent, posing a threat to people's health and sustainable socio-economic development, and preventing and controlling Lake Reservoir pollution is extremely urgent. Only by qualitative identification and quantitative analysis of the pollution sources of lake reservoir type water sources and tracing back to the main sources of pollutants can the main contradictions and key cruxes of drinking water source pollution be clarified, scientific, effective and targeted comprehensive prevention and control countermeasures be put forward, and the safety guarantee ability and level of drinking water source areas be improved. Therefore, this paper establishes a water quality monitoring system based on the depth convolution neural network (CNN), obtains the remote sensing image of a lake by combining remote sensing technology, corrects the remote sensing image pixels through CNN algorithm, and helps to monitor the water quality of the lake area by analyzing the distribution of suspended solids, TP, TN in the image.

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Section: Hierarchical Structure Of Deep Cnnmentioning

confidence: 99%

Deep Convolution Neural Network in Remote Sensing Monitoring of Water Source Pollution Sources

2022

WPPCP

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“…However, international carbon markets have not resulted in financial returns sufficiently large to motivate the full potential of land sector changes, offering an opportunity for progress. Last but not least, farm advisors should be able to translate EO information into services, adapt those services to specific local circumstances, and design plans offering a prescription for precision farming [17].…”

Section: Understanding the Governance Framework To Implement And Monitor Soil-related Policiesmentioning

confidence: 99%

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

et al. 2021

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We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.

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“…Machine learning methods are employed widely in remote sensing [1]. In particular, agricultural monitoring via remote sensing draws significant attention for various purposes ranging from early forecasting of crop yield amount [2] to the estimation of soil composite [3].…”

Section: Introductionmentioning

confidence: 99%

Predicting Soil Properties from Hyperspectral Satellite Images

Kuzu

Albrecht

Arnold

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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The AI4EO HYPERVIEW challenge seeks machine learning methods that predict agriculturally relevant soil parameters (K, Mg, P 2 O 5 , pH) from airborne hyperspectral images. We present a hybrid model fusing Random Forest and Knearest neighbor regressors that exploit the average spectral reflectance, as well as derived features such as gradients, wavelet coefficients, and Fourier transforms. The solution is computationally lightweight and improves upon the challenge baseline by 21.9%, with the first place on the public leaderboard. In addition, we discuss neural network architectures and potential future improvements.

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Soil monitoring for precision farming using hyperspectral remote sensing and soil sensors

Cited by 3 publications

References 23 publications

Deep Convolution Neural Network in Remote Sensing Monitoring of Water Source Pollution Sources

Deep Convolution Neural Network in Remote Sensing Monitoring of Water Source Pollution Sources

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

Predicting Soil Properties from Hyperspectral Satellite Images

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