###### Abstract  
Crop monitoring and smart spraying have become indispensable parts of precision agriculture where unmanned aerial vehicles (UAVs) play a lead role. In particular, in large agricultural fields, aerial monitoring is a sustainable solution provided it can be performed in an energy-efficient manner. The existing literature points out that the research on precision agriculture using UAVs is still very minimal. In this article, we propose a support vector machine (SVM)-based UAV location management technique where UAVs change position over various portions or regions of a large agricultural field so that crops are properly monitored in an energy-efficient manner. Whenever a processing request is generated from any sensor in a part of the field, the UAV investigates with an SVM to decide whether to move on to the center of that field based on various parameters or characteristics such as region-id, packet-id, time of day, waiting times of the packets, the average waiting time of others within a predefined time window, location of the UAV, residual energy of the UAV after processing the packet, and movement after processing the packet. We use 70% of our data for training and the other 30% for testing. In our simulation study, we use accuracy, precision, and recall to measure in both contexts to determine the efficiency of the model, and also the amount of energy preserved is computed corresponding to every move. We also compare our approach with current state-of-the-art energy-preserving UAV movement control techniques which are compatible with the present application scenario. The proposed technique produced 6.5%, 34.5%, and 61.5% better results in terms of percentage of successful detection (PSD), composite energy consumption (CEC), and average delay (ADL), respectively. crop monitoring; energy efficient UAV; precision agriculture; SVM; trajectory optimization; UAV application; UAV-IoT 2023  
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[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]. with UAVs [19; 20]. IoT devices catch signals from the respective lands on which they are deployed and these signals are sent to the UAV for further processing. After receiving these signals, the UAVs process those and take action accordingly. Earlier remote sensing was heavily used in cultivating fields where images were taken through satellite for various purposes such as monitoring vegetation status, irrigation, contamination detection of crops due to several diseases, and use of fertilizers [21; 22]. In spite of all these applications, the process of remote sensing by capturing satellite images was not that efficient because of poor spatial resolution of images, huge costs due to frequently taking images of all the sites, and the complexity of analyzing each one of those with equal importance. However, the task can be simplified, and notifying farmers can be performed in a timely fashion in case of the presence of predatory insects, pests, etc., provided the tracking area is divided into smaller regions and the regions are classified as sensitive or insensitive based on history. Sensitive regions should be allotted more spatial and temporal resolution, that is, those regions should be revisited by the satellite more frequently and data should be captured with enough precision to detect even the small features. This method has been elaborated on and presented in our present article. SVM is used for classifying various regions of large agricultural lands as sensitive or insensitive from the history of earlier images processed by the UAV. Images are captured by a sensor and transmitted to the UAV whose cost is far lesser than satellite imagery.