| 1. |
In Refs [28,29,30], the authors presented the UAV thermal imaging applications in precision agriculture to determine the type of thermal radiation needed for the use and interpretation of thermal data. |
Still, the extensive agricultural regions can benefit from cutting-edge technology, including the usage of UAVs. |
| 2. |
A technique based on modules for Just-In-Time Specifications of client orders in the aviation manufacturing sector was introduced in Refs [31–32] to provide the freedom to choose a product’s modules precisely when needed depending on the lead times of each module. |
However, it needs to be expanded by including ad hoc and short lead time choices as well as by carrying out the planning in accordance with the presented planning processes. |
| 3. |
Drone with IoT for agricultural fields to improve crop quality by combining drones with other machine learning and IoT ideas, the potential for future advancement is increased [33]. |
The drone can charge while it is functioning on the pitch throughout the day by placing solar panels, which eliminates the requirement for external charging. The categorization of crops and plants based on yield may be another potential use for the SVM. |
| 4. |
A compact drone-based strategy, namely, a unique CornerNet strategy using DenseNet-100 as the network’s foundation was introduced in Refs [24,25,26,27,28,29,30,31,32,33,34,35]. Creating sample annotations that will subsequently be utilized for model training and allow for the initial acquisition of the region of interest. |
However, to enhance the efficacy of our system for fine-grained pest classification, it must create a more potent feature fusion strategy. |
| 5. |
Inexpensive remote sensing instruments and techniques were developed in Ref. [36] to aid smallholder farmers with the study of vegetation factors like NDVI or cropping areas. |
Making the system more user-friendly, however, will enable consumers to collect reliable data without paying extra, which will take a lot of work. More education and lobbying efforts are required to make UAV-based remote sensing a common method of acquiring agricultural data. |
| 6. |
Adaptive precision agriculture monitoring method development using drone and satellite data for employing drone data and openly accessible satellite data (Landsat 8), the categorization of sparse and dense fields was carried out [37]. |
The suggested technique’s accuracy is decreased by certain misclassified dense pixels that are created near the boundary of the sparse class or the field. To address this weakness, the suggested approach must be enhanced further. |
| 7. |
A cutting-edge drone-based remote sensing system, integrated data processing, algorithms, and applications to smart farming were introduced in Ref. [38]. Three imaging modules—multispectral, thermal, and visible video imagers—as well as a high-performance drone are included in the system. |
The systematic integration of drone-based remote sensing with drone-based management practices, such as site-specific application of agrochemicals, will be realized further given the rapid advancements in drone technology (payload, flight time, etc.). |
