利用机器学习进行作物病害预测

Anuja Nanda, Sangam Nayak, A. Patra, Abhipsha Nanda, Saswata Pani, Bhabani Shankar Nanda
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摘要

食物是每个生物的基本需求。随着人口的增加,有必要生产足够多的粮食来满足他们的饥饿。与此同时,考虑到如此庞大的人口的需求,我们面临着很多问题。许多农作物被破坏,影响了农作物的整体产量,从而导致粮食短缺。我们知道植物受到疾病的影响是很常见的。一些因素是化肥和农药、文化习俗、环境和周围条件等。这些病害不仅影响总产量,也影响以总产量为基础的经济。任何解决这个问题的方法都将有助于农民有效地种植作物。因此,作物病害的检测在农业中起着至关重要的作用。本文旨在利用深度学习概念中的分类算法对作物病害进行检测。一种自动检测植物病害症状的技术将对农业社会非常有益,因为它可以消除持续监测的工作。在本文中,我们将提出不同的疾病分类算法,可用于植物叶片的疾病检测。
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Crop Disease Prediction by Using Machine Learning
Food is a basic need for every living being. With the increasing population, it has become necessary to yield enough amount of crops to satiate their hunger. In the mean while we face a lot of problems while considering the needs of such a huge population. Lots of crops gets destroyed which affects the overall yield of the crops, hence leading to shortage of food. We know that it is very common for plants to get affected by diseases. Some of the factors are fertilizers and pesticides, cultural practices, environmental and surrounding conditions etc. These diseases affect overall yield as well as the economy based on it. Any approach to overcome this problem would help the farmers to cultivate crops efficiently. Hence, detection of disease in crops plays a vital role in agriculture. The proposed manuscript aims for the detection of crop diseases by using classification algorithm in deep learning concept. An automatic technique to detect symptoms of plant diseases would be highly beneficial to the agricultural society as it would eliminate the work of constant monitoring. In this paper, we will propose different disease classification algorithms which can be used for the detection of disease in plant leaves.
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