Efficacy of Deep Learning Algorithm in Classifying Chilli Plant Growth Stages

Rozilan, Danial Mirza Muammar; Hanafi, Marsyita; Ali, Roslizah; Razak, Mohd Adib; Hairu, Cui

doi:10.36877/aafrj.a0000238

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…They used a dataset of only 150 images to train, and the detection result is relatively good, implying that the accuracy achieved is good (97.96%), but the author's weakness is the small dataset used. The efficacy of detection using a deep learning algorithm in classifying chilli plant growth stages was presented in 12 . The authors extracted various deep learning methods used in the study, including Inception V3, ResNet50, and VGG16, and the results show that these methods performed well in terms of accuracy and stability when tested on a dataset containing 2320 images of capsicum plants at various growth stages and imaging conditions.…”

Section: Related Workmentioning

confidence: 99%

CPD-CCNN: classification of pepper disease using a concatenation of convolutional neural network models

Bezabh,

Salau,

Abuhayi

et al. 2023

Sci Rep

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Agricultural products are vital to the sustainability of the economies of developing countries. Most developing countries’ economies such as Ethiopia heavily rely on agriculture. On a global scale, the pepper crop is one of the most important agricultural products in terms of human food security. However, it is susceptible to a variety of diseases which include blight leaf disease, gray leaf spot, common rust, fruit rot disease, powdery mildew symptoms on pepper leaf, and other related diseases that are all common today. Currently, more than 34 different pepper diseases have been discovered, resulting in a 33% average yield loss in pepper cultivation. Conventionally, farmers detect the disease using visual observation but this has its own demerits as it is usually not accurate and usually time consuming. In the past, a number of researchers have presented various methods for classifying pepper plant disease, especially using image processing and deep learning techniques. However, earlier studies have shown that binary classification requires improvement as some classes were more challenging to identify than others. In this study, we propose a concatenated neural network of the extracted features of VGG16 and AlexNet networks to develop a pepper disease classification model using fully connected layers. The development of the proposed concatenated CNN model includes steps such as dataset collection, image preprocessing, noise removal, segmentation, feature extraction, and classification. Finally, the proposed concatenated CNN model was evaluated, providing a training classification accuracy of 100%, validation accuracy of 97.29%, and testing accuracy of 95.82%. In general, it can be concluded from the findings of the study that the proposed concatenated model is suitable for identifying pepper leaf and fruit diseases from digital images of pepper.

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Section: Related Workmentioning

confidence: 99%

CPD-CCNN: classification of pepper disease using a concatenation of convolutional neural network models

Bezabh,

Salau,

Abuhayi

et al. 2023

Sci Rep

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“…The results demonstrated an impressive accuracy of 99.27% for grouping leaf diseases in tomato plants. Similarly, [30] demonstrated the effectiveness of a deep learning algorithm in categorizing different growth stages of chili plants. The authors employed several deep learning techniques Inception V3, ResNet50, and VGG16, and the outcomes indicate that these methods exhibit strong performance in terms of both accuracy and consistency.…”

Section: B Transfer Learning Modelmentioning

confidence: 99%

Multi-Step Preprocessing With UNet Segmentation and Transfer Learning Model for Pepper Bell Leaf Disease Detection

AlArfaj,

Altamimi,

Aljrees

et al. 2023

IEEE Access

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Agricultural production is a cornerstone of national economies, and the prevalence of plant diseases poses a significant threat to crop yields. Timely disease detection is essential to mitigate these risks. However, manual plant observation methods are labor-intensive and time-consuming, necessitating a shift toward automated solutions. This study addresses the pressing problem of plant disease identification by leveraging advanced image processing techniques. This research begins with a comprehensive analysis of the pepper bell leaf disease dataset. Through a series of meticulously designed image processing steps, the dataset is normalized, enhancing its quality and consistency. Building upon this preprocessing, the UNET segmentation technique in conjunction with the InceptionV3 transfer learning model is employed. This novel approach yields exceptional results, with 99.48% accuracy, 99.97% precision, 99.99% recall, and 99.98% F1 scores. To objectively assess the significance of the proposed model, the performance is benchmarked against existing state-of-the-art models. The findings demonstrate the superiority of the proposed approach in the domain of plant disease identification. By automating the detection process, this research not only enhances efficiency but also enables early disease detection, thereby potentially contributing to the agricultural sector to identify crop disease and manage it efficiently. INDEX TERMS Pepper bell leaf disease; image processing; transfer learning; InceptionV3The global economy heavily relies on agriculture for it

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“…In order to reduce farmer losses, several methods for classifying chilli leaf disease have been developed. However, based on the developed model's accuracy percentage, existing models for classifying chilli leaf diseases perform moderately well [11], as explained in the next paragraph. Furthermore, existing models can only classify chilli leaf disease using leaves picked from the tree and placed on a uniform background colour.…”

Section: Introductionmentioning

confidence: 99%

Smart Agricultural Monitoring Solution for Chilli Leaf Diseases Using a Low-Cost Kinect Camera and an Improved CNN Algorithm

Ak Entuni,

Zulcaffle,

Hong Ping

et al. 2023

Jurnal Teknologi

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Chilli is extensively grown all over the globe and is particularly important as a food. One of the most difficult issues confronting chilli cultivation is the requirement for accurate identification of leaf diseases. Leaf diseases have a negative impact on chilli production quality, resulting in significant losses for farmers. Numerous Machine Learning (ML) and Convolution Neural Network (CNN) models have been developed for classifying chilli leaf diseases under uniform background and uncomplicated leaf conditions, with an average classification accuracy achieved. However, a diseased leaf usually grows alongside a cluster of other leaves, making it difficult to classify the disease. It will be easier for farmers if there is a reliable model that can classify a chilli leaf disease in a cluster of leaves. The aim of this study was to propose a model for classifying chilli leaf disease from both a uniform background and a complex cluster of leaves. Images of diseased chilli leaves are acquired using a low-cost Kinect camera, which include discoloration, grey spots, and leaf curling. The different types of chilli leaf disease are then classified using an improved ShuffleNet CNN model. With a classification accuracy of 99.82%, the proposed model outperformed the other existing models.

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Efficacy of Deep Learning Algorithm in Classifying Chilli Plant Growth Stages

Cited by 3 publications

References 31 publications

CPD-CCNN: classification of pepper disease using a concatenation of convolutional neural network models

CPD-CCNN: classification of pepper disease using a concatenation of convolutional neural network models

Multi-Step Preprocessing With UNet Segmentation and Transfer Learning Model for Pepper Bell Leaf Disease Detection

Smart Agricultural Monitoring Solution for Chilli Leaf Diseases Using a Low-Cost Kinect Camera and an Improved CNN Algorithm

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