Improving Weeds Identification with a Repository of Agricultural Pre-trained Deep Neural Networks

Rekha Sharma, A. B. M. Mehedi Hasan, Jinfeng Su, Moshiur Bhuiyan
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Abstract

This This paper aims to increase the farmers’ income and enhance the productivity of farming day-by-day so that the agriculture industry can develop at a large scale. The objective of this research is to increase the farming and seed production for the farmers in agriculture by monitoring and computer network system with reliable servers. It is crucial to solve or minimize or avoid this problem to ensure the best farming and animal husbandry across the agriculture sector to maintain seed growth for the farmers and customers without any error and monitoring. The background for this study is to provide the best services of monitoring and computer networks globally for the production and growing sectors to be involved in emerging activities to reduce the errors using computer network monitoring. Even after dealing with these difficulties, ranchers need sizeable, stable business sectors for their harvests. Appropriately, in the last and present century, people groups have begun investigating the conceivable outcomes by embracing distinctive current procedures in agribusiness. The proposed solution will address the current challenges of computer network monitoring data across the agriculture sector to understand the actual outcomes.
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利用农业预训练深度神经网络库改进杂草识别
本文旨在增加农民的收入,日益提高农业生产效率,使农业产业得以规模化发展。本研究的目的是通过具有可靠服务器的监控和计算机网络系统,为农业农民增加耕作和种子产量。解决或减少或避免这一问题至关重要,以确保整个农业部门的最佳农牧业,为农民和客户保持种子生长,没有任何错误和监控。本研究的背景是在全球范围内为生产和成长部门提供最好的监测和计算机网络服务,以参与新兴活动,以减少使用计算机网络监测的错误。即使在解决了这些困难之后,牧场主仍需要规模可观、稳定的商业部门来维持收成。恰当地说,在上个世纪和本世纪,人们已经开始通过在农业综合企业中采用独特的现行程序来调查可能的结果。提出的解决方案将解决当前计算机网络监测整个农业部门数据的挑战,以了解实际结果。
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