Smart Poultry Management

Zhao, Yang; Yang, Xiao

doi:10.1007/978-3-030-89123-7_30-1

Cited by 1 publication

(2 citation statements)

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“…Smart agriculture promotes the deep integration of modern information technology and agricultural development; it helps to realize precise crop field management, improve crop production indicators, and contribute to sustainable agricultural development [1,2]. The application of optical imaging-based crop phenotype information collection platforms and data analysis technology is an important way to build crop growth models and obtain high-dimensional and rich phenotype datasets.…”

Section: Introductionmentioning

confidence: 99%

“…By improving, optimizing, and fusing multiple algorithms, we build a soybean emergence model based on UAV RGB imagery to achieve one-stop detection and evaluation of various soybean emergence information. The specific objectives included: (1) to construct an seedlings emergence detection module to obtain the number of soybean seedlings as well as to determine the optimal UAV sampling range by using UAV to collect images at different flight altitudes; (2) to evaluate the adaptability of two deep learning models, MobileNetV2 and AlexNet, for growth stages discrimination and explore the role of image enhancement in improving data quality and increasing model accuracy; and (3) to integrate the indicators to build an ensemble learning model to determine soybean seedling emergence uniformity by calculating emergence proportion, and guide intelligent field management and precision operations for soybeans during the seedling stage.…”

Section: Introductionmentioning

confidence: 99%

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An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery

Zhang

Zhao

2023

Sensors

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Efficient detection and evaluation of soybean seedling emergence is an important measure for making field management decisions. However, there are many indicators related to emergence, and using multiple models to detect them separately makes data processing too slow to aid timely field management. In this study, we aimed to integrate several deep learning and image processing methods to build a model to evaluate multiple soybean seedling emergence information. An unmanned aerial vehicle (UAV) was used to acquire soybean seedling RGB images at emergence (VE), cotyledon (VC), and first node (V1) stages. The number of soybean seedlings that emerged was obtained by the seedling emergence detection module, and image datasets were constructed using the seedling automatic cutting module. The improved AlexNet was used as the backbone network of the growth stage discrimination module. The above modules were combined to calculate the emergence proportion in each stage and determine soybean seedlings emergence uniformity. The results show that the seedling emergence detection module was able to identify the number of soybean seedlings with an average accuracy of 99.92%, a R2 of 0.9784, a RMSE of 6.07, and a MAE of 5.60. The improved AlexNet was more lightweight, training time was reduced, the average accuracy was 99.07%, and the average loss was 0.0355. The model was validated in the field, and the error between predicted and real emergence proportions was up to 0.0775 and down to 0.0060. It provides an effective ensemble learning model for the detection and evaluation of soybean seedling emergence, which can provide a theoretical basis for making decisions on soybean field management and precision operations and has the potential to evaluate other crops emergence information.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%