Fusion of spatio-temporal UAV and proximal sensing data for an agricultural decision support system

Katsigiannis, Panagiotis A.; Galanis, George; Dimitrakos, Agathoklis; Tsakiridis, Nikolaos L.; Kalopesas, C.; Alexandridis, Thomas; Chouzouri, A.; Patakas, Angelos; Zalidis, George C.

doi:10.1117/12.2244856

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…Despite significant advances in spatial, spectral, and temporal resolution of satellite sensors, the use of satellite images is still limited in commercial agriculture production. Limited flexibility in on-demand imaging solutions, high costs, cloud cover restriction, and lack of automated or established frameworks for image analysis and application are factors affecting large-scale adoption of satellite imageries in PA [123]. These limitations have promoted interest in low-cost proximal remote sensing techniques, including UAVs.…”

Section: Historical Applications Of Remote Sensing In Agriculturementioning

confidence: 99%

“…CWSI has been extensively used for precision irrigation management in orchards [44,190]. For example, Katsigiannis et al [123] used an autonomous multi-sensor (multi-spectral and thermal sensor) UAV system to develop CWSIs maps for irrigation scheduling and management in kiwi, pomegranate, and vine fields. However, some studies have indicated that more research is needed to establish climate-soil-crop specific trigger/threshold values to enable the use of CWSI for irrigation scheduling [191].…”

Section: Water Stressmentioning

confidence: 99%

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Applications of Remote Sensing in Precision Agriculture: A Review

2020

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Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

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Section: Historical Applications Of Remote Sensing In Agriculturementioning

confidence: 99%

Section: Water Stressmentioning

confidence: 99%

Applications of Remote Sensing in Precision Agriculture: A Review

2020

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“…Here, apart from the FAO-56 equation parameters, we can consider pruning, water price and yield price as decision parameters. In reality, there is a wide range of factors that can be accommodated by the model and have been used by other authors, e.g., nutrient pollution [93], topography, soil degradation and tree density [94], remote sensing information [95,96], tree hydration status [97], tree trunk diameter [98] pests [99], and socioeconomic parameters [100]. Moreover, under current assumptions and implementation, the decision tree shows obvious routes.…”

Section: Limitationsmentioning

confidence: 99%

Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support

et al. 2020

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This paper explores methodologies for developing intelligent automated decision systems for complex processes that contain uncertainties, thus requiring computational intelligence. Irrigation decision support systems (IDSS) promise to increase water efficiency while sustaining crop yields. Here, we explored methodologies for developing intelligent IDSS that exploit statistical, measured, and simulated data. A simple and a fuzzy multicriteria approach as well as a Decision Tree based system were analyzed. The methodologies were applied in a sample of olive tree farms of Heraklion in the island of Crete, Greece, where water resources are scarce and crop management is generally empirical. The objective is to support decision for optimal financial profit through high yield while conserving water resources through optimal irrigation schemes under various (or uncertain) intrinsic and extrinsic conditions. Crop irrigation requirements are modelled using the FAO-56 equation. The results demonstrate that the decision support based on probabilistic and fuzzy approaches point to strategies with low amounts and careful distributed water irrigation strategies. The decision tree shows that decision can be optimized by examining coexisting factors. We conclude that irrigation-based decisions can be highly assisted by methods such as decision trees given the right choice of attributes while keeping focus on the financial balance between cost and revenue.

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“…Satellite remote sensing has made significant advancements in terms of spatial, temporal, and spectral resolution. However, it comes with limitations such as limited flexibility, high costs, cloud coverage constraints, and lack of automation, which have hindered its widespread adoption [4]. However, the emergence of UAVs has opened up new possibilities in remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Predicting Winter Wheat Yield with Dual-Year Spectral Fusion, Bayesian Wisdom, and Cross-Environmental Validation

Li,

Cheng,

Chen

et al. 2024

Remote Sensing

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Winter wheat is an important grain that plays a crucial role in agricultural production and ensuring food security. Its yield directly impacts the stability and security of the global food supply. The accurate monitoring of grain yield is imperative for precise agricultural management. This study aimed to enhance winter wheat yield predictions with UAV remote sensing and investigate its predictive capability across diverse environments. In this study, RGB and multispectral (MS) data were collected on 6 May 2020 and 10 May 2022 during the grain filling stage of winter wheat. Using the Pearson correlation coefficient method, we identified 34 MS features strongly correlated with yield. Additionally, we identified 24 texture features constructed from three bands of RGB images and a plant height feature, making a total of 59 features. We used seven machine learning algorithms (Cubist, Gaussian process (GP), Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM), Random Forest (RF)) and applied recursive feature elimination (RFE) to nine feature types. These included single-sensor features, fused sensor features, single-year data, and fused year data. This process yielded diverse feature combinations, leading to the creation of seven distinct yield prediction models. These individual machine learning models were then amalgamated to formulate a Bayesian Model Averaging (BMA) model. The findings revealed that the Cubist model, based on the 2020 and 2022 dataset, achieved the highest R2 at 0.715. Notably, models incorporating both RGB and MS features outperformed those relying solely on either RGB or MS features. The BMA model surpassed individual machine learning models, exhibiting the highest accuracy (R2 = 0.725, RMSE = 0.814 t·ha−1, MSE = 0.663 t·ha−1). Additionally, models were developed using one year’s data for training and another year’s data for validation. Cubist and GLM stood out among the seven individual models, delivering strong predictive performance. The BMA model, combining these models, achieved the highest R2 of 0.673. This highlights the BMA model’s ability to generalize for multi-year data prediction.

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Fusion of spatio-temporal UAV and proximal sensing data for an agricultural decision support system

Cited by 9 publications

References 6 publications

Applications of Remote Sensing in Precision Agriculture: A Review

Applications of Remote Sensing in Precision Agriculture: A Review

Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support

Predicting Winter Wheat Yield with Dual-Year Spectral Fusion, Bayesian Wisdom, and Cross-Environmental Validation

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