Search

The yield of rice crops is strongly correlated to the number of panicles per unit area. Computer vision techniques have been utilized in previous studies to count the number of panicles automatically. However, when using images captured by Unmanned Aerial Vehicles (UAVs), the height and coverage area of the UAVs can cause shrinkage of the rice panicle objects, leading to errors in feature learning and reduced counting efficacy. In addition, there is a considerable relative size difference in rice ears, which can further affect counting accuracy. To address these issues, this paper proposes an algorithm named Refined Feature Fusion for Panicle Counting (RFF-PC). This algorithm utilizes a more refined scale division by extracting and fusing optimal features according to the object size distribution. Firstly, the number of rice ears with different sizes is quantified, and a fine division of the scale is made to calculate the receptive field size of different features that need to be fused. Secondly, multi-scale convolution generates ]features at each layer and feature pyramid fusion fuses more appropriate features at different layers to improve the ability to represent objects in complex multi-scale. Redundant information is removed through channel attention. Additionally, a refined Gaussian is used to generate ground truth close to the shape of real rice panicles. Over the UFPC2019 dataset captured at the height of 5 meters, the average counting accuracy of RFF-PC is 89.80%, and the counting accuracy only decreases from 94.33% to 90.58% over the images with an increased number of rice ears from 180 to 260, which outperforms several state-of-the-art algorithms, including MCNN, CSRNet, TasselNetV2+, DSNet, and DMCount. • We investigate the multi-scale problem of relative size in rice ear counting. • We apply multi-scale convolution to generate the refined features. • We use feature pyramid fusion to integrate these refined features. • Our refined Gaussian generates the ground truth similar to the rice panicle. • Our method outperforms the state-of-the-art for ear counting on 5m-high images. [ABSTRACT FROM AUTHOR]

Volunteer cotton (VC) plants growing in the fields of inter-seasonal and rotated crops, like corn, can serve as hosts to boll weevil pests once they reach pin-head square stage (5–6 leaf stage). The VC plants therefore need to be detected, located, and destroyed or sprayed. In this paper, we present a study on using deep learning (DL) to detect VC plants in a corn field using RGB images collected with an unmanned aerial vehicle (UAV). The objectives were (i) to determine whether the YOLOv3 DL algorithm could be used for VC detection in a corn field based on UAV-derived RGB images, and (ii) to investigate the behavior of YOLOv3 on images at three different pixel scales (320 × 320, S1; 416 × 416, S2; and 512 × 512, S3). The metrics used to evaluate the results were average precision (AP), mean average precision (mAP) and F1-score at 95 % confidence level. It was found that YOLOv3 was able to detect VC plants in corn field at an average detection accuracy of more than 80 %, F1-score of 78.5 % and mAP of 80.38 %. With respect to images size, no significant differences existed for mAP among the three scales, but a significant difference was found for AP between S1 and S3 (p = 0.04) and between S2 and S3 (p = 0.02). A significant difference was also found for F1-score between S2 and S3 (p = 0.02). The overall goal of this study was to minimize boll weevil pest infestation by maximizing the true positive detection of VC plants in a corn field which is represented by the mAP values. The lack of significant differences of these at all three scales indicated that the trained YOLOv3 model can be used for VC detection irrespective of the three input image sizes. The capability of YOLOv3 to detect VC plants demonstrates the potential of DL algorithms for real-time detection and mitigation using computer vision and a spot-spray capable UAV. [ABSTRACT FROM AUTHOR]

[Display omitted] • Plant stand count methods using unmanned aerial vehicle imagery were reviewed. • Computer vision methods produced promising results in plant stand count analysis. • Several research gaps were identified and recommendations provided. • Results will be helpful to stakeholders like farmers, producers, and researchers. Plant stand count helps in estimating the yield and evaluating the planter's efficiency and seed quality. Traditional methods of counting the plants by manual measurement are time-consuming, laborious, and error-prone. In contrast, the ground-based sensing methods are limited to smaller spaces. High spatial resolution images obtained from unmanned aerial vehicles (UAV) can be used in conjunction with computer vision algorithms to evaluate plant stand count, as it directly influences the yield. In spite of the importance of high-throughput plant stand count in row crop agriculture, no synthesized information in this specific subject matter is available. Therefore, the objective of this paper was to perform a systematic literature review of the current studies that focus on evaluating plant stand count using UAV imagery to provide well-synthesized information, identify research gaps, and provide suitable recommendations. In this study, a comprehensive literature search was performed on three academic databases (Agricola, Web of Science, and Scopus), and a total of 29 articles were found based on search terms and selection criteria for review. From the systematic review, it can be concluded that: an appropriate stage after plant emergence without canopy overlap is necessary for image acquisition; optimal flying height should be selected to balance the field coverage and accuracy; L*a*b* color space can provide better segmentation; hyperspectral camera imagery can provide good discrimination; deep learning with data augmentation and transfer learning models can be used to reduce the computational time and resources; the stand count methodology that is successful with corn and cotton could be extended to other row crops and horticultural crops; and application of direct image processing and use of open-source platforms is required for stakeholder participation. The review will be helpful to the farmers, producers, and researchers in selecting and employing the UAV-based algorithms for evaluating plant stand count. [ABSTRACT FROM AUTHOR]