Abstract
Organic farming is well-known as a traditional farming method which is responsible in producing the hygienic food product. The organic farm is the integration of agricultural production and system management. Organic farming includes the usage of low pesticides, maintains low nitrate leaching in groundwater as well as surface water, uses animal wastes, and minimizes soil erosion. Crop rotation, natural pest control, and barrier nets are the conventional methods of fertilization techniques. Organic farming makes use of recycling resources comparatively to using chemical fertilizers. A natural compost, animal manure with crop rotation implementation will improve the soil quality. Thus, organic foods are highly hygienic without causing any side effects. A few challenges arise during the implementation of organic farms. These challenges are overcome only via the smart approach and planning and coordination of public and government officials. Organic seeds take a long time to grow, and it is an economically high cost to implement this type of farming. Smart transport and supply of organic products to any location is a bit difficult. The proper selection of seed and selection of land with the suitable climate and soil texture that has to be verified for cultivation is the restrains of organic agriculture. Soil moisture content and sufficient water for the crop are attained through the usage of remote sensing in agricultural farms. The food crisis can be managed via the adaptation of remote sensing. The sensor collects information about the soil, water supply, temperature, and other environmental factors to the control unit. Initially, the infected plant with leaves will be isolated as single image as leaf, and each leaf would be diagnosed with various kernel functions including Cauchy, Invmult, and Laplacian kernel. The proposed system uses image processing to capture the images, and the remote sensing sensor generates the information to the Support Vector Machine which classifies the infected plant or part of the plant from the healthy plant based on the features extracted. The proposed Support Vector Machine (SVM) used a remote sensing technique in detecting the compliance discrepancies in an organic farm.
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13 October 2023
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12517-023-11729-y
28 September 2021
An Editorial Expression of Concern to this paper has been published: https://doi.org/10.1007/s12517-021-08470-9
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This article is part of the Topical Collection on Smart agriculture and geo-informatics
This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12517-023-11729-y"
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S, M., Sharma, A., Singh, S.P. et al. RETRACTED ARTICLE: SVM-based compliance discrepancies detection using remote sensing for organic farms. Arab J Geosci 14, 1334 (2021). https://doi.org/10.1007/s12517-021-07700-4
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DOI: https://doi.org/10.1007/s12517-021-07700-4