دورية أكاديمية

An Automated Crop Growth Detection Method Using Satellite Imagery Data

التفاصيل البيبلوغرافية
العنوان: An Automated Crop Growth Detection Method Using Satellite Imagery Data
المؤلفون: Dong-Chong Hsiou, Fay Huang, Fu Jie Tey, Tin-Yu Wu, Yi-Chuan Lee
المصدر: Agriculture; Volume 12; Issue 4; Pages: 504
بيانات النشر: Multidisciplinary Digital Publishing Institute
سنة النشر: 2022
المجموعة: MDPI Open Access Publishing
مصطلحات موضوعية: multispectral satellite imagery, multitemporal satellite imagery, artificial intelligence, gradient boosting decision tree (GBDT), heading cabbage
جغرافية الموضوع: agris
الوصف: This study develops an automated crop growth detection APP, with the functionality to access the cadastral data for the target field, that was to be used for a satellite-imagery-based field survey. A total of 735 ground-truth records of the cabbage cultivation areas in Yunlin were collected via the implemented APP in order to train a deep learning model to make accurate predictions of the growth stages of the cabbage from 0 to 70 days. A regression analysis was performed by the gradient boosting decision tree (GBDT) technique. The model was trained on multitemporal multispectral satellite images, which were retrieved from the ground-truth data. The experimental results show that the mean average error of the predictions is 8.17 days, and that 75% of the predictions have errors less than 11 days. Moreover, the GBDT algorithm was also adopted for the classification analysis. After planting, the cabbage growth stages can be divided into the cupping, early heading, and mature stages. For each stage, the prediction capture rate is 0.73, 0.51, and 0.74, respectively. If the days of growth of the cabbages are partitioned into two groups, the prediction capture rate for 0–40 days is 0.83, and that for 40–70 days is 0.76. Therefore, by applying appropriate data mining techniques, together with multitemporal multispectral satellite images, the proposed method can predict the growth stages of the cabbage automatically, which can assist the governmental agriculture department to make cabbage yield predictions when creating precautionary measures to deal with the imbalance between production and sales when needed.
نوع الوثيقة: text
وصف الملف: application/pdf
اللغة: English
العلاقة: Digital Agriculture; https://dx.doi.org/10.3390/agriculture12040504Test
DOI: 10.3390/agriculture12040504
الإتاحة: https://doi.org/10.3390/agriculture12040504Test
حقوق: https://creativecommons.org/licenses/by/4.0Test/
رقم الانضمام: edsbas.4F01B0FC
قاعدة البيانات: BASE