SMART PLANT HEALTH CARE SYSTEM: Image Based Disease Detection and Pesticide Remediation

Rohan S Savadakar
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Abstract

Plant and tree populations must be preserved and supported in order to mitigate the growing issues brought about by food and water scarcity brought on by population increase and climate change. The occurrence of plant diseases is a major issue in agriculture as it severely reduces agricultural output. In order to overcome this difficulty, scientists are investigating novel approaches that make use of sensors and imaging to collect data on plant health in order to detect diseases early on. The goal of this project is to create a "Smart Plant Health Care System" that combines embedded technologies such as Arduino, Raspberry Pi, and Jetson Nano for pesticide remediation controlled by Arduino and image-based illness diagnosis. More specifically, convolutional neural networks (CNNs) are implemented for real-time illness diagnosis using the processing capacity and adaptability of the Raspberry Pi. Keywords: Smart Agriculture, Plant Health Monitoring, Disease Detection,
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智能植物保健系统:基于图像的病害检测和杀虫剂修复
必须保护和支持植物和树木种群,以缓解人口增长和气候变化带来的粮食和水资源短缺所造成的日益严重的问题。植物病害的发生是农业的一个主要问题,因为它会严重降低农业产量。为了克服这一困难,科学家们正在研究利用传感器和成像技术收集植物健康数据的新方法,以便及早发现病害。该项目的目标是创建一个 "智能植物健康护理系统",将 Arduino、Raspberry Pi 和 Jetson Nano 等嵌入式技术结合起来,实现由 Arduino 控制的农药修复和基于图像的疾病诊断。更具体地说,利用 Raspberry Pi 的处理能力和适应性,实现了卷积神经网络(CNN),用于实时疾病诊断。关键词智能农业 植物健康监测 病害检测
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