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Automated Estimation of Crop Yield Using Artificial Intelligence and Remote Sensing Technologies

Agriculture is the backbone of any country, and plays a viable role in the total gross domestic product (GDP). Healthy and fruitful crops are of immense importance for a government to fulfill the food requirements of its inhabitants. Because of land diversities, weather conditions, geographical loca...

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Autores principales: Ilyas, Qazi Mudassar, Ahmad, Muneer, Mehmood, Abid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9952812/
https://www.ncbi.nlm.nih.gov/pubmed/36829619
http://dx.doi.org/10.3390/bioengineering10020125
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author Ilyas, Qazi Mudassar
Ahmad, Muneer
Mehmood, Abid
author_facet Ilyas, Qazi Mudassar
Ahmad, Muneer
Mehmood, Abid
author_sort Ilyas, Qazi Mudassar
collection PubMed
description Agriculture is the backbone of any country, and plays a viable role in the total gross domestic product (GDP). Healthy and fruitful crops are of immense importance for a government to fulfill the food requirements of its inhabitants. Because of land diversities, weather conditions, geographical locations, defensive measures against diseases, and natural disasters, monitoring crops with human intervention becomes quite challenging. Conventional crop classification and yield estimation methods are ineffective under unfavorable circumstances. This research exploits modern precision agriculture tools for enhanced remote crop yield estimation, and types classification by proposing a fuzzy hybrid ensembled classification and estimation method using remote sensory data. The architecture enhances the pooled images with fuzzy neighborhood spatial filtering, scaling, flipping, shearing, and zooming. The study identifies the optimal weights of the strongest candidate classifiers for the ensembled classification method adopting the bagging strategy. We augmented the imagery datasets to achieve an unbiased classification between different crop types, including jute, maize, rice, sugarcane, and wheat. Further, we considered flaxseed, lentils, rice, sugarcane, and wheat for yield estimation on publicly available datasets provided by the Food and Agriculture Organization (FAO) of the United Nations and the Word Bank DataBank. The ensemble method outperformed the individual classification methods for crop type classification on an average of 13% and 24% compared to the highest gradient boosting and lowest decision tree methods, respectively. Similarly, we observed that the gradient boosting predictor outperformed the multivariate regressor, random forest, and decision tree regressor, with a comparatively lower mean square error value on yield years 2017 to 2021. Further, the proposed architecture supports embedded devices, where remote devices can adopt a lightweight classification algorithm, such as MobilenetV2. This can significantly reduce the processing time and overhead of a large set of pooled images.
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spelling pubmed-99528122023-02-25 Automated Estimation of Crop Yield Using Artificial Intelligence and Remote Sensing Technologies Ilyas, Qazi Mudassar Ahmad, Muneer Mehmood, Abid Bioengineering (Basel) Article Agriculture is the backbone of any country, and plays a viable role in the total gross domestic product (GDP). Healthy and fruitful crops are of immense importance for a government to fulfill the food requirements of its inhabitants. Because of land diversities, weather conditions, geographical locations, defensive measures against diseases, and natural disasters, monitoring crops with human intervention becomes quite challenging. Conventional crop classification and yield estimation methods are ineffective under unfavorable circumstances. This research exploits modern precision agriculture tools for enhanced remote crop yield estimation, and types classification by proposing a fuzzy hybrid ensembled classification and estimation method using remote sensory data. The architecture enhances the pooled images with fuzzy neighborhood spatial filtering, scaling, flipping, shearing, and zooming. The study identifies the optimal weights of the strongest candidate classifiers for the ensembled classification method adopting the bagging strategy. We augmented the imagery datasets to achieve an unbiased classification between different crop types, including jute, maize, rice, sugarcane, and wheat. Further, we considered flaxseed, lentils, rice, sugarcane, and wheat for yield estimation on publicly available datasets provided by the Food and Agriculture Organization (FAO) of the United Nations and the Word Bank DataBank. The ensemble method outperformed the individual classification methods for crop type classification on an average of 13% and 24% compared to the highest gradient boosting and lowest decision tree methods, respectively. Similarly, we observed that the gradient boosting predictor outperformed the multivariate regressor, random forest, and decision tree regressor, with a comparatively lower mean square error value on yield years 2017 to 2021. Further, the proposed architecture supports embedded devices, where remote devices can adopt a lightweight classification algorithm, such as MobilenetV2. This can significantly reduce the processing time and overhead of a large set of pooled images. MDPI 2023-01-17 /pmc/articles/PMC9952812/ /pubmed/36829619 http://dx.doi.org/10.3390/bioengineering10020125 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ilyas, Qazi Mudassar
Ahmad, Muneer
Mehmood, Abid
Automated Estimation of Crop Yield Using Artificial Intelligence and Remote Sensing Technologies
title Automated Estimation of Crop Yield Using Artificial Intelligence and Remote Sensing Technologies
title_full Automated Estimation of Crop Yield Using Artificial Intelligence and Remote Sensing Technologies
title_fullStr Automated Estimation of Crop Yield Using Artificial Intelligence and Remote Sensing Technologies
title_full_unstemmed Automated Estimation of Crop Yield Using Artificial Intelligence and Remote Sensing Technologies
title_short Automated Estimation of Crop Yield Using Artificial Intelligence and Remote Sensing Technologies
title_sort automated estimation of crop yield using artificial intelligence and remote sensing technologies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9952812/
https://www.ncbi.nlm.nih.gov/pubmed/36829619
http://dx.doi.org/10.3390/bioengineering10020125
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