Abstract-Monitoring and preserving air quality has become one of the most essential activities in many industrial and urban areas today. The quality of air is adversely affected due to various forms of pollution caused by transportation, electricity, fuel uses etc. The deposition of harmful gases is creating a serious threat for the quality of life in smart cities. With increasing air pollution, we need to implement efficient air quality monitoring models which collect information about the concentration of air pollutants and provide assessment of air pollution in each area. Hence, air quality evaluation and prediction has become an important research area. The quality of air is affected by multi-dimensional factors including location, time, and uncertain variables. Recently, many researchers began to use the big data analytics approach due to advancements in big data applications and availability of environmental sensing networks and sensor data. The aim of this research paper is to investigate various big-data and machine learning based techniques for air quality forecasting. This paper reviews the published research results relating to air quality evaluation using methods of artificial intelligence, decision trees, deep learning etc. Furthermore, it throws light on some of the challenges and future research needs.Index Terms-Air quality evaluation, big data analytics, data-driven air quality evaluation, and air quality prediction.

Chlorophyll content in plant leaves is an essential indicator of the growth condition and the fertilization management effect of naked barley crops. The soil plant analysis development (SPAD) values strongly correlate with leaf chlorophyll contents. Unmanned Aerial Vehicles (UAV) can provide an efficient way to retrieve SPAD values on a relatively large scale with a high temporal resolution. But the UAV mounted with high-cost multispectral or hyperspectral sensors may be a tremendous economic burden for smallholder farmers. To overcome this shortcoming, we investigated the potential of UAV mounted with a commercial digital camera for estimating the SPAD values of naked barley leaves. We related 21 color-based vegetation indices (VIs) calculated from UAV images acquired from two flight heights (6.0 m and 50.0 m above ground level) in four different growth stages with SPAD values. Our results indicated that vegetation extraction and naked barley ears mask could improve the correlation between image-calculated vegetation indices and SPAD values. The VIs of ‘L*,’ ‘b*,’ ‘G-B’ and ‘2G-R-B’ showed significant correlations with SPAD values of naked barley leaves at both flight heights. The validation of the regression model showed that the index of ‘G-B’ could be regarded as the most robust vegetation index for predicting the SPAD values of naked barley leaves for different images and different flight heights. Our study demonstrated that the UAV mounted with a commercial camera has great potentiality in retrieving SPAD values of naked barley leaves under unstable photography conditions. It is significant for farmers to take advantage of the cheap measurement system to monitor crops.

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Chlorophyll is one of the primary pigments of plant leaves, and changes in its content can be used to characterize the physiological status of plants. Spectral indices have been devised and validated for estimating leaf chlorophyll content (LCC). However, most of the existing spectral indices do not consider the influence of angular reflection on the accuracy of the LCC estimation. In this study, the spectral reflectance factors of leaves from three plant species were measured from several observations in the principal plane. The relationship between the existing spectral indices and the LCC from different directions suggests that the directional reflection of a leaf surface impacts the accuracy of its LCC estimation. Subsequently, the ratio of reflectance differences, that is, the modified Datt index, was tested to reduce the directional reflection effect when predicting LCC. Our results indicated that the modified Datt index not only estimated LCC with high accuracy for all observation directions and plant species but also consistently predicted the LCC of each species in individual observation directions. Our method opens the possibility for optical detection of LCC using multiangular spectral reflection, which is convenient for plant science studies focused on the variation in LCC.

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