Detection and Classification of Crop Diseases from Its Leaves Using Image Processing

Mallick, Debasish Kumar; Ray, Ratula; Dash, Satya Ranjan

doi:10.1007/978-981-13-9282-5_20

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Additionally, the use of preprocessing techniques significantly improves segmentation accuracy. The k-means algorithm is the most widely and commonly used technique [29,58,[61][62][63] for segmenting diseased leaves and classifying crop diseases. In practice, no generalizable algorithm is able to solve all issues, so choosing a suitable learning algorithm for a specific problem is a crucial step for the model efficiency.…”

Section: Comparison Of Various Crop Disease Detection Techniquesmentioning

confidence: 99%

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

2021

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The agricultural sector remains a key contributor to the Moroccan economy, representing about 15% of gross domestic product (GDP). Disease attacks are constant threats to agriculture and cause heavy losses in the country’s economy. Therefore, early detection can mitigate the severity of diseases and protect crops. However, manual disease identification is both time-consuming and error prone, and requires a thorough knowledge of plant pathogens. Instead, automated methods save both time and effort. This paper presents a contemporary overview of research undertaken over the past decade in the field of disease identification of different crops using machine learning, deep learning, image processing techniques, the Internet of Things, and hyperspectral image analysis. Additionally, a comparative study of several techniques applied to crop disease detection was carried out. Furthermore, this paper discusses the different challenges to be overcome and possible solutions. Then, several suggestions to address these challenges are provided. Finally, this research provides a future perspective that promises to be a highly useful and valuable resource for researchers working in the field of crop disease detection.

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Section: Comparison Of Various Crop Disease Detection Techniquesmentioning

confidence: 99%

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

2021

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“…This disease can reduce rice productivity by up to 30% (Tariq et al 2012;Singh et al 2017;Jatoi et al 2018), and leaf smut caused by the fungus Entylomaoryzae. This disease can reduce rice productivity by 50% (Mallick et al 2018;Prajapati et al 2017;Mukherje et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Detection of Rice Plants Diseases Using Convolutional Neural Network (CNN)

Rasjava

Sugiyarto

Kurniasari

et al. 2020

icse

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As a rice-producing plant, rice plant (Oryza sativa L.) is one of the most important crops in Indonesia. Rice production is increasing every year along with an increase in rice demand and population.The amount of rice production is affected by the condition of the rice plants. The worse the condition of rice plants, the rice production will also lower. Rice plant is very susceptible to diseases or pests that can reduce its productivity, including brown spot disease, leaf smut and bacterial leaf blight. As the development of science and technology, currently known as Artificial Intelligence. Artificial intelligence is a combination of several scientific disciplines such as mathematics, statistics, computer science, and even social science. Using artificial intelligence, the system now have the ability to interpret external data correctly to learn from the data and then use the learning to achieve certain goals through flexible adaptation. The artificial intelligence fields consists of several branches, such as machine learning and deep learning. Neural Network (NN) is one of the methods used in the deep learning.NN has many types, one of which is the Convolutional Neural Network (CNN). CNN is the best-knownmethod used for processingimages data compared to other types of NN. Therefore, in this study the identification of rice plants diseases was carriedout using CNN method. From this study,better results were obtained compared to other methods, obtaining 100% accuracy for training data and 86,67% for testing data. The model obtained by the CNN method can be used for detecting 3 different types of rice plants diseases, there are brown spots, leaf smuts, or bacterial leaf blight disease based on the physical images of rice plant leaves.

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Automatic identification of diseases in grains crops through computational approaches: A review

Manavalan¹

2020

Computers and Electronics in Agriculture

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Detection and Classification of Crop Diseases from Its Leaves Using Image Processing

Cited by 4 publications

References 13 publications

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

Detection of Rice Plants Diseases Using Convolutional Neural Network (CNN)

Automatic identification of diseases in grains crops through computational approaches: A review

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