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在农业疾病诊断中整合深度学习以获得视觉问题答案:小麦生的案例研究

Akash Nanavaty¹, Rishikesh Sharma², Bhuman Pandita²

¹Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, Rajasthan, 333031, India.

Scientific reports

|November 15, 2024

概括

这项研究引入了一种新的深度学习 (DL) 和视觉问题答案 (VQA) 系统,用于快速检测小麦生. 联合的ResNet模型实现了97.69%的准确性,为精密农业提供了一个轻量级的解决方案.

科学领域:

农业科学农业科学
计算机科学计算机科学
植物病理学植物病理学

背景情况:

小麦生严重影响全球小麦产量,需要高效的诊断工具.
检测小麦生的传统方法往往耗时,需要专门的专业知识.
快速准确的疾病诊断对于有效的作物管理和粮食安全至关重要.

研究的目的:

开发和评估一种使用深度学习 (DL) 和视觉问题答案 (VQA) 进行农业疾病诊断的新方法.
专门针对在小麦作物中检测小麦生.
创建一种通用,普遍适用的模型,用于在不同条件下识别疾病.

主要方法:

WheatRustDL2024数据集 (7998张图像) 的开发,用于基于VQA的小麦生检测.
使用Flower框架与ResNet中央模型的联合学习.
启动语言图像预训练 (BLIP) 和双重注意力机制的实施.
创建一个定制的WheatRustVQA数据集 (1800个增强图像) 用于性能评估.

主要成果:

一个微调的ResNet模型在WheatRustDL2024数据集上实现了97.69%的准确性.
在WheatRustVQA测试分区上,VQA系统显示平均BLEU分数为0.6235.
联合型号轻量级,适合在没有额外硬件的情况下部署在移动设备和无人机上.

结论:

将DL与VQA集成为农业疾病诊断提供了一个有希望的,加速的,独立于专家的方法.
这种方法通过提供快速和准确的关于作物健康的见解来增强精准农业.
开发的系统有潜力通过改善植物疾病管理,为应对全球粮食安全挑战做出重大贡献.

关键词:

深度学习是一种深度学习.植物疾病植物疾病视觉问题解答视觉问题解答小麦生是因为小麦生.

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