Semi‐automatic leaf disease detection and classification system for soybean culture

Sandeep, K.; Pandey, Shreelekha; Goel, Shivani

doi:10.1049/iet-ipr.2017.0822

Cited by 159 publications

(52 citation statements)

References 22 publications

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“…From that point onward, the distance between each data, point and newly acquired cluster centres were recalculated and lastly, the process ends to obtain the clusters. One of the focal points of using K-Means segmentation is if there are a tremendous measure of factors, K-means clustering is quicker than other clustering methods [27].…”

Section: K-meansmentioning

confidence: 99%

Severity Estimation of Plant Leaf Diseases Using Segmentation Method

Entuni¹,

Zulcaffle²,

Kipli³

2020

j.asep

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Plants have assumed a significant role in the history of humankind, for the most part as a source of nourishment for human and animals. However, plants typically powerless to different sort of diseases such as leaf blight, gray spot and rust. It will cause a great loss to farmers and ranchers. Therefore, an appropriate method to estimate the severity of diseases in plant leaf is needed to overcome the problem. This paper presents the fusions of the Fuzzy C-Means segmentation method with four different colour spaces namely RGB, HSV, L*a*b and YCbCr to estimate plant leaf disease severity. The percentage of performance of proposed algorithms are recorded and compared with the previous method which are K-Means and Otsu’s thresholding. The best severity estimation algorithm and colour space used to estimate the diseases severity of plant leaf is the combination of Fuzzy C-Means and YCbCr color space. The average performance of Fuzzy C-Means is 91.08% while the average performance of YCbCr is 83.74%. Combination of Fuzzy C-Means and YCbCr produce 96.81% accuracy. This algorithm is more effective than other algorithms in terms of not only better segmentation performance but also low time complexity that is 34.75s in average with 0.2697s standard deviation.

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Section: K-meansmentioning

confidence: 99%

Severity Estimation of Plant Leaf Diseases Using Segmentation Method

Entuni¹,

Zulcaffle²,

Kipli³

2020

j.asep

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show abstract

“…These works show that the imaging technique is suited for inspection or analysis based on external features of the subject of interest. For instance, bruise detection [56,57] and disease detection [58,59] are performed by inspecting the external damage on the sample. In addition, quantitative analysis [60,61] is performed using the spatial information obtained.…”

Section: Optics and Photonics Applications In Agriculturementioning

confidence: 99%

Applications of Photonics in Agriculture Sector: A Review

et al. 2019

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The agricultural industry has made a tremendous contribution to the foundations of civilization. Basic essentials such as food, beverages, clothes and domestic materials are enriched by the agricultural industry. However, the traditional method in agriculture cultivation is labor-intensive and inadequate to meet the accelerating nature of human demands. This scenario raises the need to explore state-of-the-art crop cultivation and harvesting technologies. In this regard, optics and photonics technologies have proven to be effective solutions. This paper aims to present a comprehensive review of three photonic techniques, namely imaging, spectroscopy and spectral imaging, in a comparative manner for agriculture applications. Essentially, the spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization. This review also comprehends the practicality of all three techniques by presenting existing examples in agricultural applications. Furthermore, the potential of these techniques is reviewed and critiqued by looking into agricultural activities involving palm oil, rubber, and agro-food crops. All the possible issues and challenges in implementing the photonic techniques in agriculture are given prominence with a few selective recommendations. The highlighted insights in this review will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.

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“…In the paper [15], the authors have used K-means clustering and support vector machine algorithms to predict and classify soybean leaf disease. The diseased leaves are divided into Septoria leaf blight, downy mildew, and frog eye.…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Detection through the Implementation of Diversified and Modified Neural Network Algorithms

et al. 2021

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In the era of artificial systems, disease detection is becoming easier. For detecting disease, monitoring the plants 24 hours, visiting the agricultural office, or asking for help from a specialist seem difficult. This situation demands a user-friendly plant disease detection system, which allows people to detect whether the plant is diseased or not in an easier way. If the plant is diseased, a treatment plan will also be notified. In this way, people can easily save time, money, and, most importantly, plants. In this study, the researchers have collected data of vegetables from a field and applied multiple diversified Neural Network Algorithms such as CNN, MCNN, FRCNN, and, along with that, also proposed a new modified neural network architecture (ModCNN), which has produced 97.69% accuracy. The authors have also classified the bean leaf diseases into four categories according to their symptoms, which will help to identify diseases accurately.

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Semi‐automatic leaf disease detection and classification system for soybean culture

Cited by 159 publications

References 22 publications

Severity Estimation of Plant Leaf Diseases Using Segmentation Method

Severity Estimation of Plant Leaf Diseases Using Segmentation Method

Applications of Photonics in Agriculture Sector: A Review

Plant Disease Detection through the Implementation of Diversified and Modified Neural Network Algorithms

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