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

被引:20
|
作者
Ilyas, Qazi Mudassar [1 ]
Ahmad, Muneer [2 ]
Mehmood, Abid [3 ]
机构
[1] King Faisal Univ, Coll Comp Sci & Informat Technol, Dept Informat Syst, Al Hasa 31982, Saudi Arabia
[2] Woosong Univ, Endicott Coll Int Studies, Daejeon 34606, South Korea
[3] King Faisal Univ, Coll Business Adm, Dept Management Informat Syst, Al Hasa 31982, Saudi Arabia
来源
BIOENGINEERING-BASEL | 2023年 / 10卷 / 02期
关键词
precision agriculture; sensory images; data augmentation; feature extraction; deep learning; data analysis;
D O I
10.3390/bioengineering10020125
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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.
引用
收藏
页数:24
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