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AgRobots (A Combination of Image Processing and Data Analytics for Precision Pesticide Use)

机译:AgRobots(将图像处理和数据分析相结合以精确使用农药)

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India is mainly an agricultural country. Agriculture plays a vital role in the Indian economy. Over 70% of the rural households depend on agriculture as their principle means of livelihood. Though there is growth in other sectors, the overall share of agriculture on GDP of the country has decreased. Usually the yield is affected by many factors such as soil moisture, temperature, water salinity and by pests such as insects, fungal/bacteria/viral diseases. This problem caused by pests is overcome by using pesticides. Pesticides are the substance used for destroying insects and other organisms that are harmful for cultivated plants but the excess use of pesticides is harmful not only for surrounding environment but also for the human health. Thus the pesticides must be used in controlled manner. Here this paper mainly deals with mechanism that uses image processing technique to analyze the ill part of the plant and to provide medicine i.e. the pesticide to that part only and not to the entire plant. Initially photos of plant are clicked from different angle and these images are analyzed one by one to check if any part of the plant is attacked by any virus or bacteria. In this analysis if any part of the plant is found infected then pesticide is sprayed only to that part not to the entire plant. This way pesticide used is confined only to infected part and the excess use of pesticide is controlled. Here open CV-Python is used. Deep learning is also employed. Deep learning is a part of a broader family of machine learning based on learning data representation and learning can be semi or fully supervised. Here the general purpose computer used is Raspberry Pi. As the result of this project the amount of pesticide used is reduced by spraying pesticide to only the affected part of the plant.
机译:印度主要是农业国。农业在印度经济中起着至关重要的作用。 70%以上的农村家庭以农业为主要生计手段。尽管其他部门有所增长,但农业在该国国内生产总值中所占的比重有所下降。通常,产量受许多因素的影响,例如土壤湿度,温度,水盐度以及害虫,例如昆虫,真菌/细菌/病毒病。通过使用农药可以克服由虫害引起的这个问题。农药是用于破坏对栽培植物有害的昆虫和其他生物的物质,但是过量使用农药不仅对周围环境有害,而且对人体健康也有害。因此,必须以受控方式使用农药。在此,本文主要涉及使用图像处理技术来分析植物病害部分并仅向该部分而不是整个植物提供药物即农药的机制。最初,从不同角度单击植物的照片,然后逐张分析这些图像,以检查植物的任何部分是否受到任何病毒或细菌的攻击。在此分析中,如果发现植物的任何部分被感染,则仅将农药喷洒到该部分,而不是整个植物。这样使用的农药仅限于受感染的部分,并控制了农药的过量使用。这里使用开放的CV-Python。还使用深度学习。深度学习是基于学习数据表示的更广泛的机器学习家族的一部分,并且可以对学习进行半监督或全监督。这里使用的通用计算机是Raspberry Pi。该项目的结果是,仅向工厂的受影响部分喷洒农药,从而减少了农药的使用量。

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