Multispectral Remote Sensing as a Tool to Support Organic Crop Certification: Assessment of the Discrimination Level between Organic and Conventional Maize

Abstract

The annual certification of organic agriculture products includes an in situ inspection of the fields declared organic. This inspection is more difficult, time-consuming, and costly for large farms or in production regions located in remote areas. The global objective of this research is to assess how spatial remote sensing may support the organic crop certification process by developing a method that would enable certification bodies to target for priority in situ control crop fields declared as organic but that would show on satellite imagery an appearance closer to conventional fields. For this purpose, the ability of multispectral satellite images to discriminate between organic and conventional maize fields was assessed through the use of a set of four satellite images of different spatial and spectral resolutions acquired at different crop growth stages over a large number of maize fields (32) that are part of an operational farm in Germany. In support of this main objective, a set of in situ measurements (leaf hyperspectral reflectance, chlorophyll, and nitrogen content and dry matter percentage, crop canopy cover, height, wet biomass and dry matter percentage, soil chemical composition) was conducted to characterize the nature of the biochemical and biophysical differences between organic and conventional maize fields. The results of this research showed that highly significant biochemical and biophysical differences between a large number of organic and conventional maize fields may exist at identified crop growth stages and that these differences may be sufficiently pronounced to enable the complete discrimination between crop management modes using satellite images issued from quite common multispectral satellite sensors through the use of spectral or spatial heterogeneity indices. These results are very encouraging and suggest, for the first time, that satellite images could effectively support the organic maize certification process.

Distinguishing areas of organic, biodynamic and conventional farming by means of multispectral images. A pilot study

“…The results presented here, though less categorical, are in line with those of other authors [17], showing that both spectral and spatial heterogeneity indices derived from multispectral satellite imagery enabled very efficient to full discrimination between organic and conventional maize fields.…”

“…• organic fields differ from conventional ones due to different management practices by various characteristics, the main ones being lower crop biomass and the rates of plant development, lower plant nitrogen and chlorophyll concentration, higher intrafield heterogeneity of the canopy and a higher soil organic matter content [16,17]; • free access to weekly high-resolution satellite multispectral data from Sentinel-2; • a number of developed spectral indices, which combine data from satellite camera channels and are widely used in precision agriculture; • accessible software and hardware tools for working with multispectral raster images in GIS environment; • accessible possibilities to receive orthophotographic images taken with small drones, which are necessary for additional refinement of the target polygons.…”

Supplemental LED Lighting Improves Fruit Growth and Yield of Tomato Grown under the Sub-Optimal Lighting Condition of a Building Integrated Rooftop Greenhouse (i-RTG)

“…Chlorophyll content of leaves was evaluated in the first week of June, considering three points of the first leaf right under the third fruit truss of each plant. A Chlorophyll Content Meter CCM-200 (Opti-Sciences ® , Hudson, NY, USA) was used to non-destructively estimate the content based on the ratio of light transmittance at 653 nm and at 931 nm [20].…”

Deep Learning Driven Crop Classification and Chlorophyll Content Estimation for the Nexus Food higher Productions using Multi-spectral Remote Sensing Images

“…Denis et al [11] measured how spatial remote sensing might assist the process of organic crop certification by rising a methodology that allows certification body target to priority in situ control crop field stated as organic however that display on satellite images a closer appearance to traditional fields. Therefore, the capability of multi-spectral satellite images to distinguish among conventional maize and organic fields was evaluated by using four groups of satellite images of spectral and spatial resolutions attained at various development stages of crop over a considerable amount of maize field.…”

“…It was determined by Denis et al [26] that satellite remote sensing might help the certification process for organic crop fields that look to be more traditional on satellite images but are certified as organic by the certification agency. Therefore, the capability of multi-spectral satellite images to distinguish among conventional maize and organic fields was evaluated by using four groups of satellite images of spectral and spatial resolutions attained at various development stages of crop over a considerable amount of maize field.…”