Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

Abstract

Digital farming approach merges new technologies and sensor data to optimize the quality of crop monitoring in agriculture. The successful fusion of technology and data is highly dependent on the parameter collection, the modeling adoption, and the technology integration being accurately implemented according to the specified needs of the farm. This fusion technique has not yet been widely adopted due to several challenges; however, our study here reviews current methods and applications for fusing technologies and data. First, the study highlights different sensors that can be merged with other systems to develop fusion methods, such as optical, thermal infrared, multispectral, hyperspectral, light detection and ranging and radar. Second, the data fusion using the internet of things is reviewed. Third, the study shows different platforms that can be used as a source for the fusion of technologies, such as ground-based (tractors and robots), space-borne (satellites) and aerial (unmanned aerial vehicles) monitoring platforms. Finally, the study presents data fusion methods for site-specific crop parameter monitoring, such as nitrogen, chlorophyll, leaf area index, and aboveground biomass, and shows how the fusion of technologies and data can improve the monitoring of these parameters. The study further reveals limitations of the previous technologies and provides recommendations on how to improve their fusion with the best available sensors. The study reveals that among different data fusion methods, sensors and technologies, the airborne and terrestrial LiDAR fusion method for crop, canopy, and ground may be considered as a futuristic easy-to-use and low-cost solution to enhance the site-specific monitoring of crop parameters.

Desertification in the Sahel Region: A Product of Climate Change or Human Activities? A Case of Desert Encroachment Monitoring in North-Eastern Nigeria Using Remote Sensing Techniques

“…According to the Köppenen climates classification, the study area falls within dry climates of warm semi-arid (BSh) subtypes, where the annual daily mean temperature is about 37 C recorded in December [5]. Rainfall variability is vital for crop growth and development and is also a determinant of crop distribution [18]. There is also variability in rainfall patterns; rainfall lasts for about 120 days in Northern Yobe (our study area included) and more than 140 days in the Southern part of the State [33] with an average annual rainfall of between 160-500 mm.…”

“…The affected areas coincide with some of the nation's food baskets, i.e., areas that produce and supply high volumes of crops and livestock [17]. Despite the far-reaching impacts of desertification globally, there are still limited scientific efforts to assess, monitor, and predict its progression and impacts with precision [18]. The need for timely and precise monitoring and detection of degradation processes is essential for the development of strategies and solutions to combat aridity [18] Most desert estimates are presented at international levels for collaborative planning; local and regional data are often less accurate as they are mostly derived from projections or estimates from global studies [19][20][21].…”

“…Despite the far-reaching impacts of desertification globally, there are still limited scientific efforts to assess, monitor, and predict its progression and impacts with precision [18]. The need for timely and precise monitoring and detection of degradation processes is essential for the development of strategies and solutions to combat aridity [18] Most desert estimates are presented at international levels for collaborative planning; local and regional data are often less accurate as they are mostly derived from projections or estimates from global studies [19][20][21]. Because precise data measurements at local and regional scales can in itself be expensive and laborious, there is a need to apply remote and cheaply available methods using local and regional data for such purposes [18,20,21].…”

“…The need for timely and precise monitoring and detection of degradation processes is essential for the development of strategies and solutions to combat aridity [18] Most desert estimates are presented at international levels for collaborative planning; local and regional data are often less accurate as they are mostly derived from projections or estimates from global studies [19][20][21]. Because precise data measurements at local and regional scales can in itself be expensive and laborious, there is a need to apply remote and cheaply available methods using local and regional data for such purposes [18,20,21]. Monitoring and developing appropriate intervention mechanisms for combating aridity requires robust and frequent repetitive measurements that can be achieved using remote sensing techniques [22][23][24].…”

“…Monitoring and developing appropriate intervention mechanisms for combating aridity requires robust and frequent repetitive measurements that can be achieved using remote sensing techniques [22][23][24]. Remote sensing technology is booming and undergoing continuous development regarding its reliability, remote functions and efficiency [18]. Previous remote sensing-based desertification detection and monitoring studies have focused on the use of land cover time slices to monitor the distribution of desertification or the use of vegetative indices or a combination of both [25][26][27][28].…”