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首页> 外文期刊>International Journal of Sustainable Agricultural Management and Informatics >Automated greenhouse system for tomato crop using deep learning
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Automated greenhouse system for tomato crop using deep learning

机译：自动化温室番茄作物系统使用深度学习

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摘要

In India, many farmers are facing the major problem of crop diseases. These diseases are affecting growth and the quality of the crop. The crop diseases will occur due to change in the environment variables. To reduce the impact of diseases, the farmers required to continuously monitor the health of the crop. The only solution to this problem is to monitor the health of the crop using an automated greenhouse system. In this paper, we have proposed an automated greenhouse system for tomato crop, considering the six climate variables like temperature, air humidity, soil moisture, pH value, C02, light intensity. The proposed system uses deep neural network model for recognition of change in environmental variables. The results show that the deep neuralnetwork (DNN) model is able to reach the accuracy 90% in recognition of change in environment. By monitoring environmental facts, we can able to reduce the impact of diseases and improve the quality of the tomato crop.
机译：在印度,许多农民面临的主要作物疾病的问题。影响生长和作物的质量。作物疾病发生变化造成的环境变量。疾病,农民需要不断监测作物的健康。这个问题是监控的健康使用一个自动化温室作物系统。这篇文章中,我们提出了一个自动温室番茄作物,系统考虑六个气候变量如温度、空气湿度、土壤水分、pH值、二氧化碳光强度。网络模式识别的变化环境变量。深neuralnetwork能够(款)模型达到改变的识别精度90%在环境。事实,我们可以能够减少的影响疾病和提高番茄的质量作物。

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