利用物联网和机器学习检测农业储存中的自燃

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY Inventions Pub Date : 2023-09-26 DOI:10.3390/inventions8050122
Umar Farooq Shafi, Imran Sarwar Bajwa, Waheed Anwar, Hina Sattar, Shabana Ramzan, Aqsa Mahmood
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引用次数: 0

摘要

农业储存库在长时间的储存库中燃烧对农作物的安全和质量保存造成了很大的危害。棉花储存被认为更容易燃烧,原因有很多,即微生物生长产生的热量,储存区域的放热和吸热反应,以及储存区域的极端天气条件。从长远来看,燃烧不仅增加了发生大火的可能性,而且还可能影响棉花的品质因素,如颜色、短绒长度、种子质量等。在燃烧过程中,棉花的质量属性可能偏离其正常范围。很难检测、监测和控制燃烧。物联网(IoT)为农业、医疗保健、商业分析和工业制造领域的众多研究问题提供了高效可靠的解决方案。在农业领域,物联网为作物监测、仓库保护、作物病害预防和作物产量最大化提供了各种应用。我们还使用物联网对储存区域内的自燃进行智能实时感知,以便在长时间储存期间保持棉花的质量。在目前的研究中,我们研究了储存库内的自燃,并确定了其主要原因。然后,我们提出了一种高效的基于物联网和机器学习(ML)的解决方案,用于早期感知储存中的燃烧,以便在长时间储存期间保持棉花的质量。该系统利用基于物联网的电路实时感知引起燃烧的因素,并利用高效的人工神经网络(ANN)模型预测燃烧。通过一组不同的实验验证了所提出的燃烧智能传感。该模型的准确率为99.8%,正确率为95-98%,完整性为97-99%。建议的解决方案在检测燃烧方面非常有效,使储存业主能够及时意识到燃烧危害;因此,从长远来看,它们可以改善棉花品质的储存条件。全文由五个部分组成。
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Sensing Spontaneous Combustion in Agricultural Storage Using IoT and ML
The combustion of agricultural storage represents a big hazard to the safety and quality preservation of crops during lengthy storage times. Cotton storage is considered more prone to combustion for many reasons, i.e., heat by microbial growth, exothermic and endothermic reactions in storage areas, and extreme weather conditions in storage areas. Combustion not only increases the chances of a big fire outbreak in the long run, but it may also affect cotton’s quality factors like its color, staple length, seed quality, etc. The cotton’s quality attributes may divert from their normal range in the presence of combustion. It is difficult to detect, monitor, and control combustion. The Internet of Things (IoT) offers efficient and reliable solutions for numerous research problems in agriculture, healthcare, business analytics, and industrial manufacturing. In the agricultural domain, the IoT provides various applications for crop monitoring, warehouse protection, the prevention of crop diseases, and crop yield maximization. We also used the IoT for the smart and real-time sensing of spontaneous combustion inside storage areas in order to maintain cotton quality during lengthy storage. In the current research, we investigate spontaneous combustion inside storage and identify the primary reasons for it. Then, we proposed an efficient IoT and machine learning (ML)-based solution for the early sensing of combustion in storage in order to maintain cotton quality during long storage times. The proposed system provides real-time sensing of combustion-causing factors with the help of the IoT-based circuit and prediction of combustion using an efficient artificial neural network (ANN) model. The proposed smart sensing of combustion is verified by a different set of experiments. The proposed ANN model showed a 99.8% accuracy rate with 95–98% correctness and 97–99% completeness. The proposed solution is very efficient in detecting combustion and enables storage owners to become aware of combustion hazards in a timely manner; hence, they can improve the storage conditions for the preservation of cotton quality in the long run. The whole article consists of five sections.
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来源期刊
Inventions
Inventions Engineering-Engineering (all)
CiteScore
4.80
自引率
11.80%
发文量
91
审稿时长
12 weeks
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