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
相关论文
共 50 条
  • [1] Soybean crop yield estimation using artificial intelligence techniques
    Bandeira, Poliana Maria da Costa
    Villar, Flora Maria de Melo
    Pinto, Francisco de Assis de Carvalho
    da Silva, Felipe Lopes
    Bandeira, Priscila Pascali da Costa
    ACTA SCIENTIARUM-AGRONOMY, 2024, 46
  • [2] Crop yield estimation by satellite remote sensing
    Ferencz, C
    Bognár, P
    Lichtenberger, J
    Hamar, D
    Tarcsai, G
    Timár, G
    Molnár, G
    Pásztor, S
    Steinbach, P
    Székely, B
    Ferencz, OE
    Ferencz-Arkos, I
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2004, 25 (20) : 4113 - 4149
  • [3] Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques
    Awad, Mohamad M.
    AGRICULTURE-BASEL, 2019, 9 (03):
  • [4] Small area estimation of crop yield using remote sensing satellite data
    Singh, R
    Semwal, DP
    Rai, A
    Chhikara, RS
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2002, 23 (01) : 49 - 56
  • [5] Crop yield estimation model for Iowa using remote sensing and surface parameters
    Prasad, AK
    Chai, L
    Singh, RP
    Kafatos, M
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2006, 8 (01): : 26 - 33
  • [6] Estimation of winter wheat yield by using remote sensing data and crop model
    Guo, Jianmao
    Zheng Tengfei
    Qi, Wang
    Jia, Yang
    Shi Junyi
    Zhu Jinhui
    REMOTE SENSING AND MODELING OF ECOSYSTEMS FOR SUSTAINABILITY IX, 2012, 8513
  • [7] Estimation of Corn and Soybeans Yield using Remote Sensing and Crop Yield data in the United States
    Kim, Nari
    Lee, Yang-Won
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XVI, 2014, 9239
  • [8] Estimation of urban runoff and water quality using remote sensing and artificial intelligence
    Ha, SR
    Park, SY
    Park, DH
    WATER SCIENCE AND TECHNOLOGY, 2003, 47 (7-8) : 319 - 325
  • [9] MAIZE CROP YIELD ESTIMATION WITH REMOTE SENSING AND EMPIRICAL MODELS
    Fernandez-Ordonez, Yolanda. M.
    Soria-Ruiz, J.
    2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 3035 - 3038
  • [10] Transformative Technologies in Digital Agriculture: Leveraging Internet of Things, Remote Sensing, and Artificial Intelligence for Smart Crop Management
    Fuentes-Penailillo, Fernando
    Gutter, Karen
    Vega, Ricardo
    Silva, Gilda Carrasco
    JOURNAL OF SENSOR AND ACTUATOR NETWORKS, 2024, 13 (04)