## 4 Role of UAV in Precision Pest Management
An approach is developed by integrating environmental parameters and crop growth, experimenting with image performance classification effects, and discriminating the crops affected by the pests and diseases with Landsat-8 satellite images (Bi-Temporal). The integrated model with environmental factors and temporal growth indices proved with good results of 82.6% accuracy. In addition, it performed better in discriminating damages using Landsat-8 satellite images in winter wheat crops. Further, to enhance the accuracy level of the advancement models by integrating multi-temporal remotely sensed data with multisource, which provides a detailed spatial crop pest and disease distribution to meet the current requirements of precision agriculture [23] Comparing ground based remote sensing technologies presented in Table 6. \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., 2021 [73] & avocado & Multispectral & Persea mite & Visual Inspections \\ \hline [PERSON] et al., & & RGB, & Yellow sigatoka & Visual Inspections \\
2020 [74] & Banana & Multispectral & Hessian fly & Arthropod counts \\ \hline [PERSON] et al., 2019 [50] & Wheat & Multispectral & Wheat aphid & Arthropod counts \\ \hline [PERSON] et al., 2019 [23] & Wheat & Multispectral & Wheat aphid & Arthropod counts \\ \hline [PERSON] et al., 2017 [75] & Corn & Multispectral & Stem borer & Arthropod counts \\ \hline [PERSON] et al., 2016 [76] & Corn & Multispectral & Oriental armyworm & Damage assess-counts \\ \hline [PERSON] et al., 2016 [77] & Wheat & Multispectral & Wheat stem sawfly & Arthropod counts \\ \hline [PERSON] et al., 2014 [78] & Wheat & Multispectral & Wheat aphid & Arthropod counts damage assessments \\ \hline [PERSON] et al., 2011 [79] & Wheat & Multispectral & Aphid & Arthropod counts \\ \hline [PERSON] and [PERSON], 2010 [59] & Cotton & Multispectral & Cotton aphid & Arthropod counts \\ \hline [PERSON] and [PERSON], 2006 [63] & Cotton & Multispectral & Spider mite & Arthropod counts \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Orbital (Satellite) based remote sensing. Using spectral sensors with infrared range and 50 nm sensor bandwidth in soybean fields, a cumulative abundance of A. glycines could be effectively quantified. A. glycines on soybean are detected by simulating ground-based hyperspectral data with multispectral sensors. This approach reduces the complexity and cost while compared with counts of manual aphids with potential scouting of pests in soybean and crop production systems [82]. For the last few decades, most agriculture fields are using RS technologies for precision agriculture with different applications such as crop monitoring, Prediction of Yields, and Pest Management. Further, these techniques are also used for plant stress and nutritional deficiencies. RS technologies can detect pests and insects successfully in a wide variety of crops and fields. The average usage of different types of RS Platforms is shown in Figure 10. Precision Accuracy is more important in the economic development of the agriculture field, and the accuracy yields to monitor the crop infected by the pest and quality of the crop properly. Further, the precision accuracy rate in the agriculture field by RS technologies is shown in Figure 11.