Aerial imagery-based tobacco plant counting framework for efficient crop emergence estimation
Shahid R. Qureshi W.S. Khan U.S. Munir A. Zeb A. Imran Moazzam S.
February 2024Elsevier B.V.
Computers and Electronics in Agriculture
2024#217
Crop emergence estimation at early crop growth stages is becoming increasingly important for the long-term sustainability of natural resources. It helps farmers and agricultural stakeholders in the efficient allocation of resources like water, pesticides, and fertilizers. It can be used to estimate the yield and seed quality, identify the region of potential yield losses, and make future agriculture plans. These future agriculture plans can play a crucial role in ensuring maximum crop population and yield while utilizing the same limited land and natural resources. Most of the existing plant counting frameworks require offline processing of images with computationally expensive algorithms including the structure for motion and multiview stereo to develop an orthomosaic. This study proposed a tobacco plant counting framework that directly estimates counts from aerial images and has the potential for real-time applicability. It consists of three core modules: overlap detection, plant detection, and plant counting. The overlap detection module replaces the need for computationally expensive orthomosaic formation to avoid counting repetition by overlap masking based on only visual cues. Three different methods are evaluated as core modules for finding an optimal solution for plant counting based on time complexity and accuracy. In the first method after overlap detection, semantic segmentation with U-NET is employed as a plant detection module. For plant counting, we count the connected pixels classified as plants to estimate the crop count. In the second method after overlap detection, object detection using YOLOv7 is utilized as a plant detection module followed by simply counting each detected plant. In the third method, we utilize YOLOv7 for object detection, similar to the second method. However, we introduce the SORT (Simple Online and Realtime Tracking) algorithm for object tracking. This object tracking replaces the overlap detection module making it a real-time applicable method. For plant counting, we assess the number of tracked plants. The proposed algorithm is evaluated on two distinct tobacco fields. The high-resolution aerial data is collected from tobacco fields near Peshawar, Pakistan, and is human-labelled. The first and second methods show average F1 scores of 0.947 and 0.9667, respectively, whereas the third method has the potential for real-time applicability with an average F1 score of 0.967.
Deep learning , Object detection , Object tracking , Overlap detection , Plant counting , Semantic segmentation , SORT , U-Net , YOLO
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Department of Mechatronics Engineering, National University of Sciences and Technology, Islamabad, H-12, Pakistan
Robot Design and Development Lab, National Centre of Robotics and Automation (NCRA), National University of Sciences and Technology, Islamabad, H-12, Pakistan
Department of Mechanical and Industrial Engineering, Atlantic Technological University, Galway, H91 T8NW, Ireland
Kansas State University, Manhattan, KS, United States
Kazakh British Technical University KBTU, Almaty, Kazakhstan
School of Computer Science, Technological University Dublin, Dublin, D01 K822, Ireland
Department of Mechatronics Engineering
Robot Design and Development Lab
Department of Mechanical and Industrial Engineering
Kansas State University
Kazakh British Technical University KBTU
School of Computer Science
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