外文摘要：This paper provides an overview of the recent developments in remote sensing technology and machine learning algorithms for estimating important biophysical and biochemical parameters for precision farming. The objectives are (i) to provide an overview of recent advances in remotely sensed retrieval of biophysical and biochemical parameters brought by the developments in sensor technologies and robust machine learning algorithms and (ii) to identify the sources of uncertainty in retrieving biophysical and biochemical parameters and implications for precision agriculture. The review revealed that developments in crop biophysical and biochemical parameters retrieval techniques were mainly driven by announcements and the availability of new sensors. Two ground -breaking events can be identified, i.e., the availability of Sentinel -2 and the SuperDove constellation. The two provide high temporal -high spatial resolution data relevant for site -specific management and super -spectral configuration, enabling retrieval of crop growth and health parameters. The free availability of Sentinel -2 triggered the testing of its spectral configurations and upscaling of retrieval approaches using simulated data from field spectrometers and airborne hyperspectral sensors. SuperDoves will likely reduce the cost of very high -resolution data while providing unprecedented capabilities for detailed, accurate and frequent characterisation of field variability. Studies showed that the red -edge bands and hybrid models coupling Radiative Transfer Model (RTM) and machine learning regression algorithms (MLRA) are promising for operational and accurate monitoring of stress -related crop parameters to aid time -sensitive agronomic decisions. However, such models were tested in Mediterranean climates and performed poorly in African semi -arid areas and China's temperate continental semi -humid monsoon climates. Therefore, locally -calibrated RTM models incorporating crop -type maps and other spatio-temporal constraints may reduce uncertainties when adapted to data -scarce regions. Generally, permanent experimental sites and a lack of systematic calibration data on various crops are some limiting factors to using remote sensing technologies for PA in Sub-Saharan Africa. Other complexities arise from farm configurations, such as small field sizes and mixed cropping practices. Therefore, future studies should develop generic, scalable and transferable models, especially within under -studied areas.
外文关键词：precision agriculture;remote sensing;machine learning;leaf area index;Chlorophyll content
作者：Kganyago, Mahlatse;Mhangara, Paidamwoyo;Adjorlolo, Clement;Tsoeleng, Lesiba
作者单位：Univ Witwatersrand;South African Natl Space Agcy;African Union Dev Agcy AUDA NEPAD;Univ Johannesburg
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：218
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台