Evaluating Soybean Root Health Using Residual Neural Network (ReNN) Based Image Analysis
DOI:
https://doi.org/10.54536/ajsts.v4i2.5382Keywords:
Convolution Neural Network (CoNN), Data Preprocessing System (DPS), Internet of Things (IoT), Neural Network (NN), Residual Neural Networks (ResNN)Abstract
ReNN’s layer-wise image segmentation and robust data processing address the complexities of soybean root analysis, providing valuable insights for improved crop management. Accurate assessment of soybean root health is crucial for optimal crop production and growth. This study utilizes Residual Neural Network (ReNN) image evaluation to analyze soybean root development. Field data is collected and integrated by deploying sensor-based devices (IoT Devices) to evaluate soybean crop stages. ReNN facilitates data preprocessing, layer-wise image segmentation, and effective data processing, enabling accurate assessment of root health, plant vigor, flower fragmentation, and fruit formation. This approach predicts soybean crop yield and provides valuable insights into degradation detection and decision-making for optimal soybean cultivation practices. ReNN utilizes layer-based image formation, collecting data from farm fields through sensor-based devices. The dataset encompasses various environmental and weather conditions, ensuring comprehensive coverage. Key considerations for data preprocessing include temperature, humidity, precipitation as the Weather conditions, soil type, moisture, sunlight as Environmental factors, field location, soil heterogeneity as Spatial variability, and growth stage, seasonality as Temporal variability. By integrating these factors, ReNN enables accurate evaluation of soybean root conditions, facilitating root health assessment, Plant growth monitoring, Yield prediction, and optimized cultivation practices.
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Copyright (c) 2025 Vivek Gupta, Jhankar Moolchadani, Harsh Singh Chouhan
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