Investigation on the use of ensemble learning and big data in crop identification

Ahmed, Sayed A.; Mahmoud, Amira S.; Farg, Eslam; Mohamed, Amany M.; Moustafa, Marwa; Abutaleb, Khaled; Saleh, Ahmed M.; AbdelRahman, Mohamed A. E.; Abdelsalam, Hisham M.; Arafat, S.M. Yasir

doi:10.1016/j.heliyon.2023.e13339

Cited by 8 publications

(2 citation statements)

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“…Accurate crop recognition is a prerequisite for intelligent agricultural machine operations, such as crop navigation, automatic row alignment of weeding components and precise drug application. [1][2][3][4][5][6][7] However, unlike dry-field environments, the rice-growing environment is very specific, with complex backgrounds, aquatic plants and visual disturbance conditions. 8,9 Current mainstream visual perception methods do not meet the recognition requirements for rice plants, making it difficult to apply and promote mature intelligent technologies and equipment in paddy field environments.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accurate recognition of rice plants based on visual and tactile sensing

Chen,

Dang,

Zhang

et al. 2024

J Sci Food Agric

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BACKGROUNDCrop recognition is the basis of intelligent agricultural machine operations. Visual perception methods have achieved high recognition accuracy. However, the reliability of such methods is difficult to guarantee because of the complex environment of paddy fields. Tactile sensing methods are not affected by background or environmental interference, and have high reliability. However, in an ideal environment, the recognition accuracy is not as high as that of the visual method.RESULTSTo balance the accuracy and reliability of rice plant recognition, a combined visual–tactile method was proposed in this study. A rice plant recognition device was developed with a poly(vinylidene fluoride) sensor embedded inside the device as a tactile perceptron and a graphic designed as a visual perceptron. The primary role of the tactile perceptron is to initially recognize rice plants and provide a time point for image capture for visual perception. The main role of the visual perceptron is to extract features from the captured images and recognize rice plants again. The results of tactile and visual recognition were eventually fused to achieve accurate recognition of rice plants.CONCLUSIONThe contact speed between the recognition perceptron and rice–weed was selected for the field performance test based on the real situation of paddy field operation. The results showed that the accuracy and reliability of rice plant recognition decreased as the travelling speed of the paddy field operation machine increased. The results of this study provide a basis for intelligent farm machinery operations in rice fields. © 2024 Society of Chemical Industry.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accurate recognition of rice plants based on visual and tactile sensing

Chen,

Dang,

Zhang

et al. 2024

J Sci Food Agric

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Design of a precise ensemble expert system for crop yield prediction using machine learning analytics

Tripathi,

Biswas

2024

Journal of Forecasting

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Agriculture is facing significant challenges in the development of crop yield forecasts, which are important aspects of decision‐making at the international, regional, and local levels. The area of agriculture is attracting growing attention because of increasing the demand for food supplies. To ensure future food supplies, crop yield prediction (CYP) provides the best decision‐making to assist farmers in agricultural yield forecasting efficiently. Nevertheless, CYP is a difficult endeavor because of the intricacy of the underlying mechanisms and the effect of numerous factors, including weather patterns, soil characteristics, and crop management techniques. In today's era, ensemble learning (EL) approaches have recently demonstrated significant promise for enhancing the reliability and accuracy of CYP. The success of the EL techniques depends on several facts, including how the base learner models are trained and how these are combined. This study provides important insights into the EL techniques for CYP. This paper proposes an expert system model named precise ensemble expert system for crop yield prediction (PEESCYP) to predict the best crop for agricultural land. The proposed PEESCYP model employs multiple imputation by chained equation (MICE) data imputation technique to treat the missing values of the collected dataset, the isolation forest (IF) technique for outlier detection, the ant colony optimization (ACO) technique to perform feature selection, robust scaling (RS) technique to perform data normalization, and the extra tree (ET) is used for classification to overcome the variance and overfitting problem of the single classifiers. The measurements of the proposed PEESCYP model have been collected by means of accuracy, precision, recall, and F‐1 score using a prepared dataset, which is collected from International Crops Research Institute for the Semi‐Arid Tropics (ICRISAT), and the proposed model is compared with different single‐classifier based ML models, EL models, and various existing models available in the literature. The results of this experiment underline that the proposed PEESCYP model outperforms the others.

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