Pepper Target Recognition and Detection Based on Improved YOLO v4

Tan, Zhiyuan; Chen, Bin; Sun, Liying; Xu, Huimin; Zhang, Kun; Chen, Feng

doi:10.5755/j01.itc.52.4.34183

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Regarding other versions of YOLO, the study of Tan et al [55] utilized YOLOv4-tiny with 85% precision and YOLOv4 + Mosaic + Convolutional Block Attention Module (CBAM) with data augmentation to achieve 100% precision. A total of 500 images of peppers in a natural light environment were collected with an industrial Hikvision camera, and different pepper plants were photographed from various angles during the collection process.…”

Section: -1-related Workmentioning

confidence: 99%

Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis

Moya,

Quito,

Pilco

et al. 2024

Emerg Sci J

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In recent years, the accurate identification of chili maturity stages has become essential for optimizing cultivation processes. Conventional methodologies, primarily reliant on manual assessments or rudimentary detection systems, often fall short of reflecting the plant’s natural environment, leading to inefficiencies and prolonged harvest periods. Such methods may be imprecise and time-consuming. With the rise of computer vision and pattern recognition technologies, new opportunities in image recognition have emerged, offering solutions to these challenges. This research proposes an affordable solution for object detection and classification, specifically through version 5 of the You Only Look Once (YOLOv5) model, to determine the location and maturity state of rocoto chili peppers cultivated in Ecuador. To enhance the model’s efficacy, we introduce a novel dataset comprising images of chili peppers in their authentic states, spanning both immature and mature stages, all while preserving their natural settings and potential environmental impediments. This methodology ensures that the dataset closely replicates real-world conditions encountered by a detection system. Upon testing the model with this dataset, it achieved an accuracy of 99.99% for the classification task and an 84% accuracy rate for the detection of the crops. These promising outcomes highlight the model’s potential, indicating a game-changing technique for chili small-scale farmers, especially in Ecuador, with prospects for broader applications in agriculture. Doi: 10.28991/ESJ-2024-08-02-08 Full Text: PDF

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Section: -1-related Workmentioning

confidence: 99%

Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis

Moya,

Quito,

Pilco

et al. 2024

Emerg Sci J

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Early detection of plant leaf diseases using stacking hybrid learning

Sheneamer

2024

PLoS ONE

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The early identification of pests and diseases in crops now presents a significant challenge. Different methods have been used to resolve this problem. Sticky traps and black light traps, used to identify diseases and for field monitoring, are examples of a manual procedure for analysing the diseases. A lot of time is required, and it is less effective to manually inspect larger crop fields manually. To serve requires a professional, so it is, therefore, costly. The use of sticky traps, where by bugs stick to the material upon contact, is one method of disease monitoring. A camera is used to take a picture of the sticky trap. From the picture using the average disease count, this image is then processed to ascertain the pet density for a specific time period. Such manual methods, as well as providing an effective outcome also pose a danger to the environment. This is because farmers spray pesticides in large quantities as a preventative measure. Various approaches have been used to identify diseases, including image processing and sophisticated algorithms. The most effective method of disease identification from crops is automatic detection using methods of image processing and classification algorithms for the diseases to be categorised based on different picture attributes. With a stacking stacking hybrid learning with scratch and transfer learning strategies, which is utilised in this work, a model that has already been trained is used to learn on images of diverse fruit plant leaves from the Plant Village dataset, spanning both safe samples and various illnesses. This reasearch paper used ensemble CNN and we achieved accuracy between 99.75% to 100%.

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Effective feature selection based HOBS pruned- ELM model for tomato plant leaf disease classification

Amudha,

Brindha

2024

PLoS ONE

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Tomato cultivation is expanding rapidly, but the tomato sector faces significant challenges from various sources, including environmental (abiotic stress) and biological (biotic stress or disease) threats, which adversely impact the crop’s growth, reproduction, and overall yield potential. The objective of this work is to build deep learning based lightweight convolutional neural network (CNN) architecture for the real-time classification of biotic stress in tomato plant leaves. This model proposes to address the drawbacks of conventional CNNs, which are resource-intensive and time-consuming, by using optimization methods that reduce processing complexity and enhance classification accuracy. Traditional plant disease classification methods predominantly utilize CNN based deep learning techniques, originally developed for fundamental image classification tasks. It relies on computationally intensive CNNs, hindering real-time application due to long training times. To address this, a lighter CNN framework is proposed to enhance with two key components. Firstly, an Elephant Herding Optimization (EHO) algorithm selects pertinent features for classification tasks. The classification module integrates a Hessian-based Optimal Brain Surgeon (HOBS) approach with a pruned Extreme Learning Machine (ELM), optimizing network parameters while reducing computational complexity. The proposed pruned model gives an accuracy of 95.73%, Cohen’s kappa of 0.81%, training time of 2.35sec on Plant Village dataset, comprising 8,000 leaf images across 10 distinct classes of tomato plant, which demonstrates that this framework effectively reduces the model’s size of 9.2Mb and parameters by reducing irrelevant connections in the classification layer. The proposed classifier performance was compared to existing deep learning models, the experimental results show that the pruned DenseNet achieves an accuracy of 86.64% with a model size of 10.6 MB, while GhostNet reaches an accuracy of 92.15% at 10.9 MB. CACPNET demonstrates an accuracy of 92.4% with a model size of 18.0 MB. In contrast, the proposed approach significantly outperforms these models in terms of accuracy and processing time.

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Pepper Target Recognition and Detection Based on Improved YOLO v4

Cited by 4 publications

References 12 publications

Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis

Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis

Early detection of plant leaf diseases using stacking hybrid learning

Effective feature selection based HOBS pruned- ELM model for tomato plant leaf disease classification

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