## 4 Role of UAV in Precision Pest Management
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**Crop Name**} & \multicolumn{3}{c}{**Parameters**} \\ \cline{3-5}  & & **Camera** & **Pest Name** & **Observations** \\ \hline [PERSON] et al., 2011a,b, 2013, 2015 [54; 55; 56] & Wheat & Multispectral & Russian wheat aphid & Visual inspections \\ \hline [PERSON] et al., 2014 [57] & Wheat & Hyper spectral & Russian wheat aphid & Visual inspection of images \\ \hline [PERSON] and [PERSON] 2010 [58] & Cotton & Multispectral, Hyper spectral & Cotton aphid & Arthropod counts \\ \hline [PERSON] et al., 2009 [59] & Wheat & Multispectral & Greenbug & Arthropod counts or visual inspection \\ \hline [PERSON] 2008 [60] & Corn & Hyper spectral & European corn borer & Damage assessments \\ \hline [PERSON] et al., 2007 [61] & Wheat & Multispectral & Russian wheat aphid & Proportion of infested plants \\ \hline [PERSON], 2006 [62] & Cotton & Multispectral, Hyper spectral & Spider mite & Arthropod counts \\ \hline [PERSON] et al., 2005 [63] & Cotton & Multispectral & Tarnished plantbug & Sweep net sampling \\ \hline [PERSON] et al., 2004 [34] & Cotton & Hyper spectral & Strawberry spider & Arthropod counts \\ \hline [PERSON] et al., 2003 [64] & Cotton & Multispectral & Beet armyworm & Arthropod counts \\ \hline [PERSON] et al., 2002 [65] & Soya Bean & Multispectral & Soya Bean Cyst & Visual inspection of images \\ \hline [PERSON] et al., 1999 [66] & Cotton & Multispectral & Tarnished plant bug & Sweep net sampling, drop cloth sampling \\ \hline [PERSON] et al., 1997 [67] & Grape & Multispectral & Grape phylloxera & Root digging \\ \hline [PERSON] and [PERSON], 1968 [68] & Citrus & Multispectral & Brown soft scale & Arthropod counts sooty mold assessments \\ \hline [PERSON] et al., 1994 [69] & Citrus & Multispectral & Citrus blackfly & Visual inspections sooty mold assessments \\ \hline [PERSON] et al., 1996 [70] & Cotton & Multispectral & Silverleaf whitefly & Visual inspections sooty mold assessments \\ \hline [PERSON] et al., 1973 [71] & Citrus & Multispectral & Citrus blackfly & Arthropod counts sooty mold assessments \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._in sorghum fields [72] Comparing orbital based remote sensing technologies presented in Table 5.  
Remote sensing tools coupled with Machine Learning have a lead role in monitoring the crop and surveillance of pests. Early warning systems use remote sensing applications to classify crops and pest-affected areas that provide accurate and cost-effective data at different agricultural fields with proper spatial, temporal, and spectral resolutions. However, monitoring more significant landscapes is challenging, therefore combining high-resolution UAV satellite images of data through efficient machine learning (ML) models and advanced mobile applications, which helps detect the disease-affected part. The hybrid model system is developed by combining a custom classifier and object detection model (RetinaNet) for disease classification and banana localization; we have used RGB-UAV aerial images from the Republic of Benin and DR Congo fields. This result proves better accuracy under different testing with performance metrics and reveals that RGB-UAV mixed model successfully classifies the object classification and detection among healthy and diseased crops with 99.4% accuracy. Thus, this approach provides high potential support systems for making major banana diseases in Africa [76]. Monitoring the pests and diseases makes vital in providing treatment practically in affected regions. The accuracy level of the crops affected by insects and pests gets improved when the environmental parameters are coupled with the vegetation index. Furthermore, similar symptoms can be identified for different pests and diseases in crop growth. Therefore, the information of growth period helps obtain the changes incurred in the crop concerning infection of insects and pests.