Banana Plant Disease Classification Using Hybrid Convolutional Neural Network

Narayanan, K. Lakshmi; Krishnan, R. Santhana; Robinson, Y. Harold; Julie, E. Golden; Vimal, S.; Saravanan, V.; Kaliappan, M.

doi:10.1155/2022/9153699

Cited by 71 publications

(18 citation statements)

References 21 publications

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“…Support Vector Machine (SVM) classifier [15], K-means clustering as well as GLCM method and KNN classifier [16], ANN classifier [17] all techniques were used for plant disease detection and classification. But the accuracy of approaches like KNN, ANN, and other image processing algorithms needs to be improved, and they typically take more time to classify plant diseases [18]. Many diseases don't have distinct edges where they manifest themselves and instead may blend in with the healthy leaf tissue, making it difficult to detect them using current methods and necessitating the use of a powerful classification system.…”

Section: Related Workmentioning

confidence: 99%

PlantDet: A Robust Multi-Model Ensemble Method Based on Deep Learning For Plant Disease Detection

et al. 2023

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Plant disease is a significant health concern among all living creatures. Early diagnosis can help farmers takenecessary steps to cure the disease and accelerate the production rate efficiently. Our research has beenconducted with five most common rice leaf diseases, such as bacterial leaf blight, brown spot, leaf blast,leaf scald, and narrow brown spot, including healthy class, and two categories of betel leaf, such as healthyand unhealthy class. A robust new deep ensemble model, based on InceptionResNetV2, EfficientNetV2L,and Xception, has been proposed, known as, PlantDet, in this research. PlantDet solves not only underfitting problems but also leverage nourished performances simultaneously for scarce dataset of the sparse number of different background image dataset. PlantDet integrates efficient data augmentation, preprocessing, Global Average Pooling layer, Dropout mechanism, L2 regularizers, PRelu activation function, Batch Normalization layers, and more Dense layers that make the model more robust compared to all existing models and help to handle underfitting and overfitting problems while maintaining high performance. PlantDet exceeds the previous state-of-art model for the Rice Leaf dataset with an Accuracy of 98.53%, a Precision of 98.50%, a Recall of 98.35%, a F1 of 98.42% and a Specificity of 99.71%. In addition, for the Betel Leaf dataset, PlantDet also surpassed all existing base models, including several robust ensemble models. Finally, Grad-CAM and Score-CAM have been accomplished with the Xception method to explain the model performances particularly to elaborate how the Deep Learning (DL) models works for this complex dataset. Score-CAM slightly outperformed Grad-CAM++ in terms of localizing the predicted area.

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Section: Related Workmentioning

confidence: 99%

PlantDet: A Robust Multi-Model Ensemble Method Based on Deep Learning For Plant Disease Detection

et al. 2023

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“…By using Python programming in the Jupyter notebook setting with an I5 CPU, 32 GB of RAM, and 6 GB AMD GPUs from NVIDIA, the detection and categorization of banana tree ailments were evaluated. The study in (Narayanan et al, 2022) then categorizes the picture as either an infected leaves or a healthy leaf using the attributes identified by the fusion-based SVM testing approach. When a fresh picture is offered as a search query and the classification decides on its own in phase P1 if the given test image is a real image, the procedure is complete.…”

Section: Figure 3: Study Identification Prisma Flowchartmentioning

confidence: 99%

Artificial Intelligence Techniques for the Pest Detection in Banana Field: A Systematic Review

Nasim,

Rashid,

Syed

et al. 2023

Pak. J. Biotechnol

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Purpose: This systematic review details the diseases that influence banana production and their detection. A common method for identifying plant diseases in plants is image processing. Segmentation is one method for using image processing to establish medical diagnosis. The main objective of this study is to identify, categorize, and evaluate several image processing techniques used to control pests in a banana crop. Methodology: An electronic search was conducted using relevant keywords on openly available databases including IEEE Xplore, PubMed, Science Direct, and Google Scholar. 104 items were discovered by the search engine. After removing the duplicates, there were 56 research papers remained, but 22 of them were discarded after title and abstract checks since they did not address insect detection in banana fields. Results: 22 papers that come under the headings of image classification, AI/ML, deep learning, and mobile applications provide usable and reliable detection techniques in this systematic review

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“…This includes plant disease classification, crop/plant recognition, flower/fruit counting, harvesting, and yield prediction. Narayanan et al [6] introduced a hybrid CNN architecture designed to classify four banana diseases, achieving a remarkable performance accuracy of 99%. Similarly, Yu et al [7] proposed Mask-RCNN, a two-stage detection model, for instance segmentation of strawberry fruit and identification of a picking point for harvesting robot.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Improved YOLOv5s-CGhostnet for Detection of Strawberry Maturity Levels and Counting

Tamrakar,

Karki,

Kang

et al. 2024

AgriEngineering

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A lightweight strawberry detection and localization algorithm plays a crucial role in enabling the harvesting robot to effectively harvest strawberries. The YOLO model has often been used in strawberry fruit detection for its high accuracy, speed, and robustness. However, some challenges exist, such as the requirement for large model sizes, high computation operation, and undesirable detection. Therefore, the lightweight improved YOLOv5s-CGhostnet was proposed to enhance strawberry detection. In this study, YOLOv5s underwent comprehensive model compression with Ghost modules GCBS and GC3, replacing modules CBS and C3 in the backbone and neck. Furthermore, the default GIOU bounding box regressor loss function was replaced by SIOU for improved localization. Similarly, CBAM attention modules were added before SPPF and between the up-sampling and down-sampling feature fusion FPN–PAN network in the neck section. The improved model exhibited higher mAP@0.5 of 91.7% with a significant decrement in model size by 85.09% and a reduction in GFLOPS by 88.5% compared to the baseline model of YOLOv5. The model demonstrated an increment in mean average precision, a decrement in model size, and reduced computation overhead compared to the standard lightweight YOLO models.

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Banana Plant Disease Classification Using Hybrid Convolutional Neural Network

Cited by 71 publications

References 21 publications

PlantDet: A Robust Multi-Model Ensemble Method Based on Deep Learning For Plant Disease Detection

PlantDet: A Robust Multi-Model Ensemble Method Based on Deep Learning For Plant Disease Detection

Artificial Intelligence Techniques for the Pest Detection in Banana Field: A Systematic Review

Lightweight Improved YOLOv5s-CGhostnet for Detection of Strawberry Maturity Levels and Counting

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