Fast detection of banana bunches and stalks in the natural environment based on deep learning

Fu, Lanhui; Wu, Fengyun; Zou, Xiangjun; Jiang, Yinlong; Lin, Jiaquan; Yang, Zhou; Duan, Jieli

doi:10.1016/j.compag.2022.106800

Cited by 33 publications

(24 citation statements)

References 32 publications

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“…They developed a citrus leaf dataset containing three types of diseases for disease detection, and achieved 89.3% mAP. YOLOV4 was applied to detect banana bunches and stems in [26], and banana fruits in [27]. Among them, refs.…”

Section: Research On Plant Detectionmentioning

confidence: 99%

Banana Pseudostem Visual Detection Method Based on Improved YOLOV7 Detection Algorithm

Cai

Liang

et al. 2023

Agronomy

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Detecting banana pseudostems is an indispensable part of the intelligent management of banana cultivation, which can be used in settings such as counting banana pseudostems and smart fertilization. In complex environments, dense and occlusion banana pseudostems pose a significant challenge for detection. This paper proposes an improved YOLOV7 deep learning object detection algorithm, YOLOV7-FM, for detecting banana pseudostems with different growth conditions. In the loss optimization part of the YOLOV7 model, Focal loss is introduced, to optimize the problematic training for banana pseudostems that are dense and sheltered, so as to improve the recognition rate of challenging samples. In the data augmentation part of the YOLOV7 model, the Mixup data augmentation is used, to improve the model’s generalization ability for banana pseudostems with similar features to complex environments. This paper compares the AP (average precision) and inference speed of the YOLOV7-FM algorithm with YOLOX, YOLOV5, YOLOV3, and Faster R-CNN algorithms. The results show that the AP and inference speed of the YOLOV7-FM algorithm is higher than those models that are compared, with an average inference time of 8.0 ms per image containing banana pseudostems and AP of 81.45%. This improved YOLOV7-FM model can achieve fast and accurate detection of banana pseudostems.

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Section: Research On Plant Detectionmentioning

confidence: 99%

Banana Pseudostem Visual Detection Method Based on Improved YOLOV7 Detection Algorithm

Cai

Liang

et al. 2023

Agronomy

Self Cite

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“…With the expansion of the number of various public datasets and the development of image processing and object detection technologies, the research on leaf disease feature location and classification based on field crop images has developed rapidly [19][20][21][22][23]. The first step of the detection process is the localization of the disease, which is mainly based on the feature information extracted by the model to locate the disease spots and judge the degree of infection.…”

Section: Related Workmentioning

confidence: 99%

Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model

et al. 2022

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Traditional plant disease diagnosis methods are mostly based on expert diagnosis, which easily leads to the backwardness of crop disease control and field management. In this paper, to improve the speed and accuracy of disease classification, a plant disease detection and classification method based on the optimized lightweight YOLOv5 model is proposed. We propose an IASM mechanism to improve the accuracy and efficiency of the model, to achieve model weight reduction through Ghostnet and WBF structure, and to combine BiFPN and fast normalization fusion for weighted feature fusion to speed up the learning efficiency of each feature layer. To verify the effect of the optimized model, we conducted a performance comparison test and ablation test between the optimized model and other mainstream models. The results show that the operation time and accuracy of the optimized model are 11.8% and 3.98% higher than the original model, respectively, while F1 score reaches 92.65%, which highlight statistical metrics better than the current mainstream models. Moreover, the classification accuracy rate on the self-made dataset reaches 92.57%, indicating the effectiveness of the plant disease classification model proposed in this paper, and the transfer learning ability of the model can be used to expand the application scope in the future.

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“…Liu et al [ 20 ] proposed a recognition method based on the improved YOLOv3, which realized fast and accurate recognition of winter jujube in natural scenarios. Fu et al [ 21 ] applied YOLOv4 to recognize banana bunches and stems in natural environments. Xu et al [ 22 ] compared the target detection effect of different backbone feature extraction networks based on YOLOv3 and selected DenseNet201 as the backbone feature to extract the network to realize the recognition and detection of popular teas.…”

Section: Introductionmentioning

confidence: 99%

Detection of Green Asparagus in Complex Environments Based on the Improved YOLOv5 Algorithm

Hong

et al. 2023

Sensors

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An improved YOLOv5 algorithm for the efficient recognition and detection of asparagus with a high accuracy in complex environments was proposed in this study to realize the intelligent machine harvesting of green asparagus. The coordinate attention (CA) mechanism was added to the backbone feature extraction network, which focused more attention on the growth characteristics of asparagus. In the neck part of the algorithm, PANet was replaced with BiFPN, which enhanced the feature propagation and reuse. At the same time, a dataset of asparagus in complex environments under different weather conditions was constructed, and the performance variations of the models with distinct attention mechanisms and feature fusion networks were compared through experiments. Experimental results showed that the mAP@0.5 of the improved YOLOv5 model increased by 4.22% and reached 98.69%, compared with the YOLOv5 prototype network. Thus, the improved YOLOv5 algorithm can effectively detect asparagus and provide technical support for intelligent machine harvesting of asparagus in different weather conditions and complex environments.

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Fast detection of banana bunches and stalks in the natural environment based on deep learning

Cited by 33 publications

References 32 publications

Banana Pseudostem Visual Detection Method Based on Improved YOLOV7 Detection Algorithm

Banana Pseudostem Visual Detection Method Based on Improved YOLOV7 Detection Algorithm

Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model

Detection of Green Asparagus in Complex Environments Based on the Improved YOLOv5 Algorithm

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