An Automatic Coffee Plant Diseases Identification Using Hybrid Approaches of Image Processing and Decision Tree

Mengistu, Abrham Debasu; Mengistu, Seffi Gebeyehu; Alemayehu, Dagnachew Melesew

doi:10.11591/ijeecs.v9.i3.pp806-811

Cited by 13 publications

(5 citation statements)

References 6 publications

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“…They also used transfer learning as a methodology for the uses of a pre-trained neural network to gain knowledge at the time of training one dataset to be used on other datasets and Categorical cross-entropy for losing functions to evaluate the performance in the model. In research [7], the authors were focused on creating a knowledge-based system by extracting knowledge of experts to make rules using decision tree methods and image processing. Images are given as input and prepossessing the image to minimize low frequency, background noise, reflection, and masking portion of the images to get good accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…They also used texture features like GLCM (energy, entropy, con-trast, homogeneity, correlation, shade, prominence) and Color features. In research [8], the authors used back propagation algorithm with the descending gradient method to reduce the error function by adjusting the network weights, for their entire work they evaluating different approaches based on deep learning for the problem of segmentation, classification, and quantification of biotic stress of coffee leaves. In research [9], the researchers used computational methods as like GLCM such as energy, correlation, contrast, and homogeneity as well as local binary pattern and deep learning to detect Cercospora and Rust coffee plant disease.…”

Section: Related Workmentioning

confidence: 99%

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Coffee Leaf Plant Disease Identification through Image Processing and Machine-Learning Techniques in Ethiopia.

Taye,

Goel

2024

Preprint

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Abstract—Coffee plants are woody evergreens that can reach a height of up to ten meter’s in the wild with the fruit of coffee beans, which are the seeds that produce the majority of the world’s coffee. This study focuses on a variety of Arabica Coffee leaf diseases that occurs frequently in Ethiopia. The diseases which occurred primarily are Miner coffee leaf disease (MCLD), Rust coffee leaf disease (RCLD), Cercospora coffee leaf disease (CCLD), and Phoma coffee leaf disease (PCLD). The study focuses on the identification of those four a variety of diseases through image processing as well as machine learning mechanisms through Transfer learning and convolution neural network (CNN) architectures through the feed-forward model, resnet50 model, inceptionV3 model, and deep learning model through tenser flow, which are the most popular models to detect various plant diseases with high Accuracy. All the pictures used for this study were captured from the whole state of Ethiopia in every place where coffee plant exists. The total number of data sets currently used is 58,546 with 80 percent being put to use for training, while the rest 20 percent were employed for testing, with a 99.9%, 98.5%, 99%, and 99% respectively with a total success rate in classification accuracy and 99.8%, 98%, 99% and 99.7% respectively with total success rate in confusion matrix accuracy. So there is excellent performance in inferences.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Coffee Leaf Plant Disease Identification through Image Processing and Machine-Learning Techniques in Ethiopia.

Taye,

Goel

2024

Preprint

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“…A working memory and inference engine to provide limitations to the professional system was developed. Another group of researchers developed a system using the DT methods in which symptoms of coffee plant diseases were classified 15 . Preprocessing techniques were also applied.…”

Section: Related Workmentioning

confidence: 99%

“…Another group of researchers developed a system using the DT methods in which symptoms of coffee plant diseases were classified. 15 Preprocessing techniques were also applied. This includes reducing low-frequency background noise, standardizing individual particle intensities, and masking portions of the images.…”

Section: Machine Learning-based Approachesmentioning

confidence: 99%

A novel deep learning architecture for disease classification in Arabica coffee plants

Ramamurthy

Thekkath

Batra

et al. 2023

Concurrency and Computation

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Several research works on disease detection in coffee plants have been presented in recent years. Leaf miner and rust are the most prevalent diseases in Arabica coffee plants. Early detection of such diseases allows farmer to take diagnostic actions before the infection spreads to neighboring plants. With advancements in drones and artificial intelligence (AI), the automatic detection of leaf diseases is gaining prominence in the field of smart agriculture. Furthermore, it is critical to develop an accurate method for infestation detection with minimal computational complexity. Existing works for plant disease detection utilize pre-trained deep learning models with millions of parameters.A feasible trade-off has to be attained between accuracy and computational complexity for the deployment of such deep networks. This research proposes an effective method for disease detection in Arabica coffee plants using EfficientNetB0 architecture. The architecture of the EfficientNetB0 network was improvised by including a ghost module at its end. This integration allows the network to learn effectively with minimal parameters without compensating for the end accuracy. The proposed model has a total of 4,874,531 parameters which is significantly lesser than most of the state-of-the-art deep learning architectures and achieved an accuracy of 84%.

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“…The previous research only used the texture feature as the one image feature in identifying the type of a plant. The study of the feature characteristic extraction towards the Ethiopia coffee plant disease was done with HSV color space where the features of the coffee leaves had different color variations [14]. The previous research did not use the shape feature which can visually show that a very different plant has a different leaf shape from other leaves.…”

Section: Leaf Feature Extractionmentioning

confidence: 99%

Identification of Plant Types by Leaf Textures Based on the Backpropagation Neural Network

Hidayat

Nilawati

2018

IJECE

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The number of species of plants or flora in Indonesia is abundant. The wealth of Indonesia's flora species is not to be doubted. Almost every region in Indonesia has one or some distinctive plant(s) which may not exist in other countries. In enhancing the potential diversity of tropical plant resources, good management and utilization of biodiversity is required. Based on such diversity, plant classification becomes a challenge to do. The most common way to recognize between one plant and another is to identify the leaf of each plant. Leaf-based classification is an alternative and the most effective way to do because leaves will exist all the time, while fruits and flowers may only exist at any given time. In this study, the researchers will identify plants based on the textures of the leaves. Leaf feature extraction is done by calculating the area value, perimeter, and additional features of leaf images such as shape roundness and slenderness. The results of the extraction will then be selected for training by using the backpropagation neural network. The result of the training (the formation of the training set) will be calculated to produce the value of recognition accuracy with which the feature value of the dataset of the leaf images is then to be matched. The result of the identification of plant species based on leaf texture characteristics is expected to accelerate the process of plant classification based on the characteristics of the leaves.

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An Automatic Coffee Plant Diseases Identification Using Hybrid Approaches of Image Processing and Decision Tree

Cited by 13 publications

References 6 publications

Coffee Leaf Plant Disease Identification through Image Processing and Machine-Learning Techniques in Ethiopia.

Coffee Leaf Plant Disease Identification through Image Processing and Machine-Learning Techniques in Ethiopia.

A novel deep learning architecture for disease classification in Arabica coffee plants

Identification of Plant Types by Leaf Textures Based on the Backpropagation Neural Network

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