State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter

Zhang, Tianxiang; Su, Jinya; Liu, Cunjia; Chen, Wenhua

doi:10.1016/j.compag.2020.105909

Cited by 22 publications

(12 citation statements)

References 28 publications

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“…The use of UAVs for agriculture and plant biosecurity is rapidly increasing [1][2][3][4][5][6] and the use of UAV remote sensing for precision agriculture (PA) has grown dramatically [7]. The use of unmanned aerial vehicles (UAVs) for remote sensing (RS) has developed rapidly as a method of capturing high-resolution images from the near surface of the Earth [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery

et al. 2022

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The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS techniques have been successfully utilized to evaluate the chlorophyll content for the monitoring of sugarcane crops. This research provides a new framework for inferring the chlorophyll content in sugarcane crops at the canopy level using unmanned aerial vehicles (UAVs) and spectral vegetation indices processed with multiple machine learning algorithms. Studies were conducted in a sugarcane field located in Sugarcane Research Institute (SRI, Uda Walawe, Sri Lanka), with various fertilizer applications over the entire growing season from 2020 to 2021. An UAV with multispectral camera was used to collect the aerial images to generate the vegetation indices. Ground measurements of leaf chlorophyll were used as indications for fertilizer status in the sugarcane field. Different machine learning (ML) algorithms were used ground-truthing data of chlorophyll content and spectral vegetation indices to forecast sugarcane chlorophyll content. Several machine learning algorithms such as MLR, RF, DT, SVR, XGB, KNN and ANN were applied in two ways: before feature selection (BFS) by training the algorithms with all twenty-four (24) vegetation indices with five (05) spectral bands and after feature selection (AFS) by training algorithms with fifteen (15) vegetation indices. All the algorithms with both BFS and AFS methods were compared with an estimated coefficient of determination (R2) and root mean square error (RMSE). Spectral indices such as RVI and DVI were shown to be the most reliable indices for estimating chlorophyll content in sugarcane fields, with coefficients of determination (R2) of 0.94 and 0.93, respectively. XGB model shows the highest validation score (R2) and lowest RMSE in both methods of BFS (0.96 and 0.14) and AFS (0.98 and 0.78), respectively. However, KNN and SVR algorithms show the lowest validation accuracy than other models. According to the results, the AFS validation score is higher than BFS in MLR, SVR, XGB and KNN. Even though, validation score of the ANN model is decreased in AFS. The findings demonstrated that the use of multispectral UAV could be utilized to estimate chlorophyll content and measure crop health status over a larger sugarcane field. This methodology will aid in real-time crop nutrition management in sugarcane plantations by reducing the need for conventional measurement of sugarcane chlorophyll content.

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Section: Introductionmentioning

confidence: 99%

Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery

et al. 2022

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“…This highlights the need for good calibration practices such that end-users can determine if the simulations are sufficiently reliable for their particular applications ( Seidel et al., 2018 ; Gao et al., 2020 ), and model misspecification can be avoided ( Wallach, 2011 ). Because of the large model uncertainties, multiple additional sources of information are recommended to adjust and improve model simulations for farmers’ fields based on real time observations ( Zhang et al., 2021 ). This should capture field, cultivar and growing period specific dynamics which cannot always be captured based on collected calibration data.…”

Section: Discussionmentioning

confidence: 99%

Usefulness of cultivar-level calibration of AquaCrop for vegetables depends on the crop and data availability

et al. 2023

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As a result of climate change, climatic extremes are expected to increase. For high-value crops like vegetables, irrigation is a potentially economically viable adaptation measure in western Europe. To optimally schedule irrigation, decision support systems based on crop models like AquaCrop are increasingly used by farmers. High value vegetable crops like cauliflower or spinach are grown in two distinct growth cycles per year and, additionally, have a high turnover rate of new varieties. To successfully deploy the AquaCrop model in a decision support system, it requires a robust calibration. However, it is not known whether parameters can be conserved over both growth periods, nor whether a cultivar dependent model calibration is always required. Furthermore, when data are collected from farmers’ fields, there are constraints in data availability and uncertainty. We collected data from commercial cauliflower and spinach fields in Belgium in 2019, 2020 and 2021 during different growing periods and of different cultivars. With the use of a Bayesian calibration, we confirmed the need for a condition or cultivar specific calibration for cauliflower, while for spinach, splitting the data per cultivar or pooling the data together did not improve uncertainty on the model simulations. However, due to uncertainties arising from field specific soil and weather conditions, or measurement errors from calibration data, real time field specific adjustments are advised to simulations when using AquaCrop as decision support tool. Remotely sensed or in situ ground data may be invaluable information to reduce uncertainty on model simulations.

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“…Jika klausanya adalah premis sesuai dengan situasi (TRUE), maka prosesnya adalah untuk menegaskan kesimpulan [6]. Forward Chaining merupakan salah satu teknik yang digunakan dalam mekanisme inferensi untuk pengujian aturan [7].…”

Section: Metode Forward Chainingunclassified

Sistem Penunjang Keputusan Diagnosa Kerusakan Printer Pada PT. Primamakmur Langgeng Perkasa Dengan Metode Forward Chaining

Parma¹,

Affandi²,

Novita³

et al. 2022

JNKTI

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Abstrak - Dalam teknologi informasi, sistem pengambilan keputusan adalah ilmu komputer yang memungkinkan mesin bekerja seperti manusia. Sistem pengambilan keputusan adalah sistem yang menggunakan basis pengetahuan para ahli di bidangnya (seperti dokter, teknisi, dll) untuk dimasukkan ke dalam komputer untuk memecahkan masalah yang biasanya diselesaikan oleh para ahli melalui metode kecerdasan buatan. Printer merupakan kebutuhan penting untuk mendukung aktivitas sehari-hari ,kerusakan printer yang sering dialami pengguna sangat menghambat kelancaran pekerjaannya. Kerusakan tersebut memerlukan penanganan yang tepat dan tepat, dan jika tidak segera ditangani akan merugikan pengguna. Metode Forward Chaining adalah metode untuk metode pencarian, atau teknik pelacakan ke depan, dimulai dengan informasi yang ada dalam bentuk data nyata, dan kemudian bergerak maju melalui premis dan aturan kombinatorial untuk menghasilkan kesimpulan (penalaran bawah ke atas) atau tujuan. Oleh karena itu, sangat penting dibangun sebuah sistem pengambil keputusan yang terkomputerasi yang dapat memudahkan dalam menentukan kerusakan printer yang sesuai kriteria permasalahan pada kerusakan printer agar dapat mengidentifikasi permasalahan kerusakan printer dengan lebih cepat. Hasil dari penelitian ini bermanfaat bagi pengguna yaitu para karyawan dan staff, khususnya karyawan yang ingin menanggulangi ketika printer rusak dengan cepat dan efisien. karena dengan aplikasi ini pengguna dapat mengidentifikasi kerusakan printer dengan melihat gejala-gejala yang terlihat. Hasil dari identifikasi, memberikan hasil berupa prediksi awal yang dapat digunakan untuk memperbaiki kerusakan printer.Kata Kunci: Sistem Penunjang Keputusan, Kerusakan Printer, Forward Abstract - In information technology, decision-making systems are computer science that allows machines to work like humans. Decision-making systems are systems that use the knowledge base of experts in their fields (such as doctors, technicians, etc.) to be entered into computers to solve problems that are usually solved by experts through artificial intelligence methods. Printers are an important need to support daily activities, printer damage that is often experienced by users greatly hampers the smooth running of their work. Such damage requires proper and appropriate handling, and if not treated immediately will be detrimental to the user. Forward Chaining method is a method for searching methods, or forward tracking techniques, starting with existing information in the form of real data, and then moving forward through combinatorial premises and rules to produce conclusions (bottom-up reasoning) or goals. Therefore, it is very important to build a computerized decision-making system that can make it easier to determine printer damage according to the problem criteria for printer damage in order to identify printer damage problems more quickly. The results of this study are useful for users, namely employees and staff, especially employees who want to deal with when the printer is damaged quickly and efficiently. because with this application users can identify printer damage by looking at the visible symptoms. The results of the identification, provide results in the form of initial predictions that can be used to repair printer damage.Keywords : Decision Support System, Printer Damage, Forward Chaining

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State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter

Cited by 22 publications

References 28 publications

Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery

Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery

Usefulness of cultivar-level calibration of AquaCrop for vegetables depends on the crop and data availability

Sistem Penunjang Keputusan Diagnosa Kerusakan Printer Pada PT. Primamakmur Langgeng Perkasa Dengan Metode Forward Chaining

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