Crop model- and satellite imagery-based recommendation tool for variable rate N fertilizer application for the US Corn system

Jin, Zhenong; Prasad, Rishi; Shriver, John M.; Zhuang, Qianlai

doi:10.1007/s11119-016-9488-z

Cited by 54 publications

(26 citation statements)

References 52 publications

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“…One of the extensive crops in which precision management has gained adepts due to economic and environmental considerations is maize (Zea mays L.) as stated by Jin et al [18]. One of the most frequent applications of PA techniques is the use of multi-spectral images to improve in-season N management and yield prediction [19].…”

Section: Introductionmentioning

confidence: 99%

Use of Farmer Knowledge in the Delineation of Potential Management Zones in Precision Agriculture: A Case Study in Maize (Zea mays L.)

2018

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Abstract:One of the fields of research in precision agriculture (PA) is the delineation of potential management zones (PMZs, also known as site-specific management zones, or simply management zones). To delineate PMZs, cluster analysis is the main used and recommended methodology. For cluster analysis, mainly yield maps, remote sensing multispectral indices, apparent soil electrical conductivity (ECa), and topography data are used. Nevertheless, there is still no accepted protocol or guidelines for establishing PMZs, and different solutions exist. In addition, the farmer's expert knowledge is not usually taken into account in the delineation process. The objective of the present work was to propose a methodology to delineate potential management zones for differential crop management that expresses the productive potential of the soil within a field. The Management Zone Analyst (MZA) software, which implements a fuzzy c-means algorithm, was used to create different alternatives of PMZ that were validated with yield data in a maize (Zea mays L.) field. The farmers' expert knowledge was then taken into account to improve the resulting PMZs that best fitted to the yield spatial variability pattern. This knowledge was considered highly valuable information that could be also very useful for deciding management actions to be taken to reduce within-field variability.

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Section: Introductionmentioning

confidence: 99%

Use of Farmer Knowledge in the Delineation of Potential Management Zones in Precision Agriculture: A Case Study in Maize (Zea mays L.)

2018

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“…Therefore, optimizing N management for maize production to minimize the adverse environmental impacts is crucially important for sustainable development of agriculture [5,10].The optimum N rate depends on crop N demand and soil N supply. The crop N demand is determined by the plant growth status and grain yield potential, while the soil N supply is a net result of mineralization, immobilization and losses of soil N. They are both influenced by many factors such as seasonal temperature, precipitation, physical and biogeochemical soil properties, and management history [11]. The interactions between soil water and N determine the growth, development and yield of maize.…”

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confidence: 99%

Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China

et al. 2019

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Precision nitrogen (N) management (PNM) strategies are urgently needed for the sustainability of rain-fed maize (Zea mays L.) production in Northeast China. The objective of this study was to develop an active canopy sensor (ACS)-based PNM strategy for rain-fed maize through improving in-season prediction of yield potential (YP 0 ), response index to side-dress N based on harvested yield (RI Harvest ), and side-dress N agronomic efficiency (AE NS ). Field experiments involving six N rate treatments and three planting densities were conducted in three growing seasons (2015)(2016)(2017) in two different soil types. A hand-held GreenSeeker sensor was used at V8-9 growth stage to collect normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). The results indicated that NDVI or RVI combined with relative plant height (NDVI*RH or RVI*RH) were more strongly related to YP 0 (R 2 = 0.44-0.78) than only using NDVI or RVI (R 2 = 0.26-0.68). The improved N fertilizer optimization algorithm (INFOA) using in-season predicted AE NS optimized N rates better than the N fertilizer optimization algorithm (NFOA) using average constant AE NS . The INFOA-based PNM strategies could increase marginal returns by 212 $ ha −1 and 70 $ ha −1 , reduce N surplus by 65% and 62%, and improve N use efficiency (NUE) by 4%-40% and 11%-65% compared with farmer's typical N management in the black and aeolian sandy soils, respectively. It is concluded that the ACS-based PNM strategies have the potential to significantly improve profitability and sustainability of maize production in Northeast China. More studies are needed to further improve N management strategies using more advanced sensing technologies and incorporating weather and soil information.Sustainability 2019, 11, 706 2 of 26 as climate change and tropospheric ozone pollution, can also negatively affect crop yields [7,8]. N fertilizer related greenhouse gas (GHG) emissions (nitrous oxide under wet conditions and ammonia under hot conditions) have exceeded the corresponding gains in soil carbon related to its effect on increased biomass by 700% in China [9]. Therefore, optimizing N management for maize production to minimize the adverse environmental impacts is crucially important for sustainable development of agriculture [5,10].The optimum N rate depends on crop N demand and soil N supply. The crop N demand is determined by the plant growth status and grain yield potential, while the soil N supply is a net result of mineralization, immobilization and losses of soil N. They are both influenced by many factors such as seasonal temperature, precipitation, physical and biogeochemical soil properties, and management history [11]. The interactions between soil water and N determine the growth, development and yield of maize. Efficient utilization of water and N can only be realized if they are closely matched [12]. Climate conditions can affect optimum N via various processes in soil such as nitrification, denitrification, leaching, and mineralization, which will modula...

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“…The planning of agricultural tasks requires a deep knowledge of crop state [2]. For example, an important but typical case is the application of variable rate nitrogen fertilizers, as discussed in [3]. Vineyards and fruit plants are also especially good examples of complex exercises in both crop detection and study for agricultural image segmentation [4] problems such as weed detection [5], nitrogen application at hot-spots and selective harvesting.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data

et al. 2019

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One of the most challenging problems in precision agriculture is to correctly identify and separate crops from the soil. Current precision farming algorithms based on artificially intelligent networks use multi-spectral or hyper-spectral data to derive radiometric indices that guide the operational management of agricultural complexes. Deep learning applications using these big data require sensitive filtering of raw data to effectively drive their hidden layer neural network architectures. Threshold techniques based on the normalized difference vegetation index (NDVI) or other similar metrics are generally used to simplify the development and training of deep learning neural networks. They have the advantage of being natural transformations of hyper-spectral or multi-spectral images that filter the data stream into a neural network, while reducing training requirements and increasing system classification performance. In this paper, to calculate a detailed crop/soil segmentation based on high resolution Digital Surface Model (DSM) data, we propose the redefinition of the radiometric index using a directional mathematical filter. To further refine the analysis, we feed this new radiometric index image of about 3500 × 4500 pixels into a relatively small Convolution Neural Network (CNN) designed for general image pattern recognition at 28 × 28 pixels to evaluate and resolve the vegetation correctly. We show that the result of applying a DSM filter to the NDVI radiometric index before feeding it into a Convolutional Neural Network can potentially improve crop separation hit rate by 65%.

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Crop model- and satellite imagery-based recommendation tool for variable rate N fertilizer application for the US Corn system

Cited by 54 publications

References 52 publications

Use of Farmer Knowledge in the Delineation of Potential Management Zones in Precision Agriculture: A Case Study in Maize (Zea mays L.)

Use of Farmer Knowledge in the Delineation of Potential Management Zones in Precision Agriculture: A Case Study in Maize (Zea mays L.)

Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China

Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data

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