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Out of the executive collection of crop diversity, Oryza sativa is one of the vital staples in our daily life. The plant O. sativa, better known as rice, is crucial to human society, culture, and nourishment, and it plays a key role in our daily life. O. sativa's (rice's) growth is intricately linked to the interaction of three important environmental elements: soil, rainfall, and temperature. These elements work together to provide the circumstances required for productive rice farming, underscoring the critical role they play in the development of this crucial crop. Farmers grow O. sativa in millions of hectares throughout the region, and many landless workers derive income from working on these farms. Soil, rainfall, and temperature play an important role for farmers in the proper growth of O. sativa. An ambient intelligent technology is introduced for agriculture where deep learning is used to classify the diseased leaves and then the loop will be executed to check the cause of the disease and fluctuation the fertilizer dose accordingly. We have used Raspberry Pi3 to monitor the soil variation and to check the disease and pest moments YOLO v3 algorithm is implemented along with the hue saturation value with radial basis function network for soil variation and fertilizer dose check. In order to give farmers, agronomists, and other stakeholder’s timely and accurate information for making informed decisions about crop management, resource allocation, risk assessment, and overall farm productivity, it combines a variety of data sources, analytical models, and computational algorithms. We have implemented ambient intelligence and analyzed the correlation or O. sativa with soil, humidity, temperature, and pH. Value is to understand the growth and disease of the crop in the research-related area. With the AgriDSS, we have received the accuracy of 99. 20% at 50 epochs with the loss variation of 0.0601%. Our research will help the farmers to take the precautionary measures, and the productivity will be increased accordingly.

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Deep Learning-Based Productivity Analysis for Oryza sativa with Decision Support System

  • 会议论文

  • 发表日期：

    2025年

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  • Nikita Soren,
  • P. Selvi Rajendran

摘要

Out of the executive collection of crop diversity, Oryza sativa is one of the vital staples in our daily life. The plant O. sativa, better known as rice, is crucial to human society, culture, and nourishment, and it plays a key role in our daily life. O. sativa's (rice's) growth is intricately linked to the interaction of three important environmental elements: soil, rainfall, and temperature. These elements work together to provide the circumstances required for productive rice farming, underscoring the critical role they play in the development of this crucial crop. Farmers grow O. sativa in millions of hectares throughout the region, and many landless workers derive income from working on these farms. Soil, rainfall, and temperature play an important role for farmers in the proper growth of O. sativa. An ambient intelligent technology is introduced for agriculture where deep learning is used to classify the diseased leaves and then the loop will be executed to check the cause of the disease and fluctuation the fertilizer dose accordingly. We have used Raspberry Pi3 to monitor the soil variation and to check the disease and pest moments YOLO v3 algorithm is implemented along with the hue saturation value with radial basis function network for soil variation and fertilizer dose check. In order to give farmers, agronomists, and other stakeholder’s timely and accurate information for making informed decisions about crop management, resource allocation, risk assessment, and overall farm productivity, it combines a variety of data sources, analytical models, and computational algorithms. We have implemented ambient intelligence and analyzed the correlation or O. sativa with soil, humidity, temperature, and pH. Value is to understand the growth and disease of the crop in the research-related area. With the AgriDSS, we have received the accuracy of 99. 20% at 50 epochs with the loss variation of 0.0601%. Our research will help the farmers to take the precautionary measures, and the productivity will be increased accordingly.