With the development of artificial intelligence, the intelligence of agriculture has become a trend. Intelligent monitoring of agricultural activities is an important part of it. However, due to difficulties in achieving a balance between quality and cost, the goal of improving the economic benefits of agricultural activities has not reached the expected level. Farm supervision requires intensive human effort and may not produce satisfactory results. In order to achieve intelligent monitoring of agricultural activities and improve economic benefits, this paper proposes a solution that combines unmanned aerial vehicles (UAVs) with deep learning models. The proposed solution aims to detect and classify objects using UAVs in the agricultural industry, thereby achieving independent agriculture without human intervention. To achieve this, a highly reliable target detection and tracking system is developed using Unmanned Aerial Vehicles. The use of deep learning methods allows the system to effectively solve the target detection and tracking problem. The model utilizes data collected from DJI Mirage 4 unmanned aerial vehicles to detect, track, and classify different types of targets. The performance evaluation of the proposed method shows promising results. By combining UAV technology and deep learning models, this paper provides a cost-effective solution for intelligent monitoring of agricultural activities. The proposed method offers the potential to improve the economic benefits of farming while reducing the need for intensive hum.
(c) YOLOv7 Improved Faster RCNN: We customized the baseline pre-trained RCNN according to our needs. We trained the model on the drone to capture the images of various classes. We performed the transfer learning by adjusting the batch size to 10 and resizing the images to 512 512, as resizing has effects on training time as well as memory consumption, and set the number of epochs to 100. We observed that Faster RCNN faces difficulties when predicting the group objects. Faster RCNN person and cow detection is shown in Figure 11a. Faster RCNN sheep detection is shown in Figure 11b. Faster RCNN person detection is shown in Figure 11c. Improved Mask RCNN: v7. Mask RCNN person and cow detection is shown in Figure 12a. Mask RCNN sheep detection is shown in Figure 12b. Mask RCNN sheep detection is shown in Figure 12c.
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Improved SSD: To attain maximum accuracy, the baseline SSD model is improvised by changing the hyperparameters batch size to 10 and picture size to 480. With many open-source datasets, such as COCO, VisDrone, and the aerial agricultural dataset, the model was trained. To improve speed, the number of epochs was set to 50. The model was then used to recognize objects in test data after the model was trained. This algorithm produces predictions in a frame that contains bounding boxes and labels for the objects. SSD cow detection is shown in Figure 13. Agriculture 2024, 14, 522 Figure 11. (a) Faster RCNN person and cow detection. (b) Faster RCNN sheep detection. (c) Faster RCNN sheep detection. Figure 12. (a) Mask RCNN person and cow detection. (b) Mask RCNN sheep detection. (c) Mask RCNN person detection. Figure 13. SSD cow detection. 16 of 31 Agriculture 2024, 14, 522 17 of 31 The summary of model performance is shown in Table 8. Table 8. Summary of model performance. Model
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Object Training Validation Testing mAP Testing Time/Image (s) Sheep 1203 350 350 70.21% 17.20 Cow 1147 289 389 15.38% 19.10 YOLOv7+ DeepSort Person 1543 514 514 76.23% 15.14 Horse 1924 584 584 61.23% 16.13 Farming Machineries 516 172 172 63.03% 16.42 Sheep 1203 350 350 58.1% 24 Cow 1147 389 389 60.2% 22 Faster RCNN Person 1543 514 514 67.20% 21 Horse 1924 584 584 54.60% 25 Farming Machineries 516 172 172 54.70% 23 Sheep 1203 350 350 67.00% 27.2 Cow 1147 389 389 62.20% 58.8 Mask RCNN Person 1543 514 514 68.50% 25.3 Horse 1924 584 584 55.01% 26.4 Farming Machineries 516 172 172 62.00% 24.5 Sheep 1203 350 350 56% 20 Cow 1147 389 389 44% 25 SSD Person 1543 514 514 45% 27 Horse 1924 584 584 39% 22
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Farming Machineries 516 172 172 61% 21 2.5. Smart Surveillance System for Livestock Farms: Design, Implementation, Results 2.5.1. System Development and Design System Development To develop the real-time farm surveillance system multiple cloud-based platforms are utilized. Our dataset consists of images and videos in RGB format and are stored in Google cloud, which has advanced sharing, security capabilities and also is cost-effective compared to other available cloud storages. This stored data can be easily accessed by other platforms such as Google Colab.
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