Disease Detection in Coffee Plants Using Convolutional Neural Network

Kumar, Manoj; Gupta, Pranav; Madhav, P. V. L.; Sachin, .

doi:10.1109/icces48766.2020.9138000

Cited by 50 publications

(23 citation statements)

References 9 publications

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“…This condition then affects the tree damage index value. In addition, this plant is generally susceptible to wear to the location of the stems and leaves (Kumar et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

Multi-Purpose Trees Species Damage Index: Case Study of Wan Abdul Rachman Forest Park Utilization Block, Lampung

Safe’i¹,

Andrian²,

Maryono³

2022

IJSEI

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The Wan Abdul Rachman (WAR) Forest Park (TAHURA) Utilization Block is a location that can be utilized by the community, especially its non-timber forest products. Utilization of non-timber forest products is the type of Multi-Purpose Trees Species (MPTS). On the other hand, many trees were damaged due to this utilization. This study aimed to obtain the damage index (IK) of MPTS types in the TAHURA WAR utilization block. Data was collected by assessing the IK of trees based on the Forest Health Monitoring (FHM) method on five cluster plots of FHM. Data analysis was carried out using the IK value formula. The results showed that there were 13 types of MPTS found in the TAHURA WAR utilization block; with the range of IK values for each kind of MPTS as follows: Durio zibenthinus (0.0-6.1), Persea americana (1.4-6.5), Parkia speciosa (0.0-5.8), Archidendron pauciflorum (1.5-6.1), Gnetum gnemon (0.0-6.1), Hevea brasiliensis (1.4-6.1), Theobroma cacao (1.4-5.0), Mangifera indica (1.6-4.3), Sygyzium malaccensis (1.6-4.6), Arthocarpus heterophyllus (1.6-5.8), Sygyzium aqueum (1.4-4.3), Sygyzium aromaticum (1.5-3.8) and Tectona grandis (1.6-4.9). The location influences the size of the IK value, type and severity of tree damage found. Thus, the average IK values for each kind of MPTS in the TAHURA WAR utilization block were Hevea brasiliensis (4.3), Artocarpus heterophyllus (4.2), Archidendron pauciflorum (4.1), Theobroma cacao (4.0), Tectona grandis (3.8), Persea americana (3.5), Parkia speciosa (3.5), Durio zibenthinus (3.4), Sygyzium malaccensis (3.4), Gnetum gnemon (3.2), Sygyzium aqueum (2.9), Mangifera indica (2.7) dan Sygyzium aromaticum (2.4).

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“…This condition then affects the tree damage index value. In addition, this plant is generally susceptible to wear to the location of the stems and leaves (Kumar et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

Multi-Purpose Trees Species Damage Index: Case Study of Wan Abdul Rachman Forest Park Utilization Block, Lampung

Safe’i¹,

Andrian²,

Maryono³

2022

IJSEI

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“…The authors utilized four classes of images to train this classification, and VGG16 has an accuracy of up to 92.4%. Kumar et al [ 21 ] presented a system to identifying diseases at coffee leaves, and radial basis function neural network, fuzzy logic-based expert system, transfer learning techniques, and CNN with data augmentation were used for identification. This study employs two types of datasets: original leaf images and chosen symptomatic portions from leaf images.…”

Section: Related Workmentioning

confidence: 99%

Classification of Citrus Diseases Using Optimization Deep Learning Approach

Elaraby

Hamdy

Alanazi

2022

Computational Intelligence and Neuroscience

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Most plant diseases have apparent signs, and today’s recognized method is for an expert plant pathologist to identify the disease by looking at infected plant leaves using a microscope. The fact is that manually diagnosing diseases is time consuming and that the effectiveness of the diagnosis is related to the pathologist’s talents, making this a great application area for computer-aided diagnostic systems. The proposed work describes an approach for detecting and classifying diseases in citrus plants using deep learning and image processing. The main cause of decreased productivity is considered to be plant diseases, which results in financial losses. Citrus is an important source of nutrients such as vitamin C all around the world. On the contrary, citrus diseases have a negative impact on the citrus fruit and quality. In the recent decade, computer vision and image processing techniques have become increasingly popular for the detection and classification of plant diseases. The suggested approach is evaluated on the citrus disease image gallery dataset and the combined dataset (citrus image datasets of infested scale and plant village). These datasets were used to identify and classify citrus diseases such as anthracnose, black spot, canker, scab, greening, and melanose. AlexNet and VGG19 are two kinds of convolutional neural networks that were used to build and test the proposed approach. The system’s total performance reached 94% at its best. The proposed approach outperforms the existing methods.

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“…It is necessary to detect their presence as soon as possible to prevent pests from destroying all existing plants. Almost all of the filtered articles discussed the detection of diseases or the health level of coffee plants through their leaves [9,10,35,41,[45][46][47][48]50,53,54]. The rest detected insect pests based on sound [32] and detected tissue characteristics using ultrasonic waves [11].…”

Section: Iot Smart Farming Technology Solution For Coffee Farmingmentioning

confidence: 99%

The Potential of Smart Farming IoT Implementation for Coffee farming in Indonesia: A Systematic Review

Mulyono

Apnitami

Wangi

et al. 2022

Green Intell. Syst. Appl.

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As one of Indonesia’s main export agricultural commodities, coffee farming faces many obstacles, ranging from plant pest organisms to climate and environmental problems. These problems can be solved using smart farming technology. However, smart farming technology has not been applied intensively in Indonesia. This paper aims to analyze the potential for implementing smart farming for coffee in Indonesia. This article presents a systematic review of the information about the potential application of IoT smart farming for coffee farming in Indonesia by applying the PSALSAR (Protocol, Search, Appraisal, Synthesis, Analysis, Report) review method. This study concludes the list of smart farming technologies for coffee that have the potential to be applied in Indonesia. Those technologies are classified based on their application scope: quality control (including subtopics like coffee quality control), climate monitoring, the anticipation of pest organisms, and coffee processing), coffee production planning, and coffee waste utilization. Regarding infrastructure readiness and the need for smart farming technology for coffee, the island of Java has the most potential for implementing smart farming for coffee as soon as possible. The high potential for application in Java is because the telecommunications technology infrastructure is ready, and the land area and coffee production are large.

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Disease Detection in Coffee Plants Using Convolutional Neural Network

Cited by 50 publications

References 9 publications

Multi-Purpose Trees Species Damage Index: Case Study of Wan Abdul Rachman Forest Park Utilization Block, Lampung

Multi-Purpose Trees Species Damage Index: Case Study of Wan Abdul Rachman Forest Park Utilization Block, Lampung

Classification of Citrus Diseases Using Optimization Deep Learning Approach

The Potential of Smart Farming IoT Implementation for Coffee farming in Indonesia: A Systematic Review

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