Soybean Disease Detection and Segmentation Based on Mask-RCNN Algorithm Article Swipe

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Manish Kumar , Narendra Singh Chandel , Dushyant Singh , Laxman Singh Rajput ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.9734/jeai/2023/v45i52132

Anthracnose, frogeye leaf spot (FLS), rhizoctonia aerial blight (RAB), soybean mosaic virus (SMV), and yellow mosaic virus (YMV) of soybean are major common soybean leaf diseases that seriously affect soybean yield in India. However, the existing system needs a real-time detection method for soybean leaf diseases, which will help to take appropriate action for disease cure with minimum losses. This study studied a real-time detector for soybean leaf diseases based on deep convolutional neural networks. The 3,127 RGB images of disease-free leaves, anthracnose, FLS, RAB, SMV, and YMV-affected leaves of soybean were collected from the agriculture fields. The Mask R-CNN detection algorithm was used for the detection of soybean leaf diseases by introducing the Res Net 50 module. The pre-processed images (512×512 pixels) were used as input in Mask R-CNN. The model was trained at a number of epochs, training step per epoch training & validation, and learning rate were 80, 500, 50, 8, and 0.001, respectively. The detection accuracy was calculated at three levels of minimum detection confidence i.e. 0.80, 0.85, and 0.90. The results indicate that the maximum detection accuracy i.e. greater than 85% at 0.90 level of minimum detection confidence. This research indicates that the real-time detector based on deep learning provides a feasible solution for diagnosing soybean leaf diseases and provides guidance for the detection of other plant diseases. In addition to that the application of pesticide in the early stage reduce the use of pesticide resulting in less environmental pollution.

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Artificial Intelligence

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Artificial intelligence Blight Soybean mosaic virus Segmentation Leaf spot Algorithm Computer science Mathematics Horticulture Biology Plant virus Virus Virology Potyvirus

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Type: article
Language: en
Landing Page: https://doi.org/10.9734/jeai/2023/v45i52132
PDF: https://journaljeai.com/index.php/JEAI/article/download/2132/4262
OA Status: diamond
Cited By: 11
References: 12
Related Works: 10
OpenAlex ID: https://openalex.org/W4372348793

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DOI: https://doi.org/10.9734/jeai/2023/v45i52132

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Title: Soybean Disease Detection and Segmentation Based on Mask-RCNN Algorithm

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Type: article

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Language: en

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Publication year: 2023

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Publication date: 2023-04-05

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Authors: Manish Kumar, Narendra Singh Chandel, Dushyant Singh, Laxman Singh Rajput

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Landing page: https://doi.org/10.9734/jeai/2023/v45i52132

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Concepts: Artificial intelligence, Blight, Soybean mosaic virus, Segmentation, Leaf spot, Algorithm, Computer science, Mathematics, Horticulture, Biology, Plant virus, Virus, Virology, Potyvirus

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Cited by: 11

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abstract_inverted_index.three	163
abstract_inverted_index.virus	11, 16
abstract_inverted_index.which	46
abstract_inverted_index.yield	30
abstract_inverted_index.(FLS),	4
abstract_inverted_index.(RAB),	8
abstract_inverted_index.(SMV),	12
abstract_inverted_index.0.001,	155
abstract_inverted_index.India.	32
abstract_inverted_index.R-CNN.	129
abstract_inverted_index.action	52
abstract_inverted_index.aerial	6
abstract_inverted_index.affect	28
abstract_inverted_index.blight	7
abstract_inverted_index.common	22
abstract_inverted_index.images	78, 120
abstract_inverted_index.leaves	88
abstract_inverted_index.levels	164
abstract_inverted_index.method	41
abstract_inverted_index.mosaic	10, 15
abstract_inverted_index.neural	73
abstract_inverted_index.number	136
abstract_inverted_index.reduce	235
abstract_inverted_index.system	36
abstract_inverted_index.yellow	14
abstract_inverted_index.disease	54
abstract_inverted_index.epochs,	138
abstract_inverted_index.fields.	96
abstract_inverted_index.frogeye	1
abstract_inverted_index.greater	183
abstract_inverted_index.leaves,	81
abstract_inverted_index.losses.	58
abstract_inverted_index.maximum	179
abstract_inverted_index.minimum	57, 166, 190
abstract_inverted_index.module.	117
abstract_inverted_index.pixels)	122
abstract_inverted_index.results	175
abstract_inverted_index.soybean	9, 19, 23, 29, 43, 66, 90, 108, 210
abstract_inverted_index.studied	61
abstract_inverted_index.trained	133
abstract_inverted_index.However,	33
abstract_inverted_index.accuracy	159, 181
abstract_inverted_index.addition	224
abstract_inverted_index.detector	64, 199
abstract_inverted_index.diseases	25, 68, 110, 212
abstract_inverted_index.existing	35
abstract_inverted_index.feasible	206
abstract_inverted_index.guidance	215
abstract_inverted_index.indicate	176
abstract_inverted_index.learning	147, 203
abstract_inverted_index.provides	204, 214
abstract_inverted_index.research	194
abstract_inverted_index.solution	207
abstract_inverted_index.training	139, 143
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abstract_inverted_index.detection	40, 100, 106, 158, 167, 180, 191, 218
abstract_inverted_index.diseases,	45
abstract_inverted_index.diseases.	222
abstract_inverted_index.indicates	195
abstract_inverted_index.networks.	74
abstract_inverted_index.pesticide	230, 239
abstract_inverted_index.real-time	39, 63, 198
abstract_inverted_index.resulting	240
abstract_inverted_index.seriously	27
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abstract_inverted_index.confidence	168
abstract_inverted_index.diagnosing	209
abstract_inverted_index.pollution.	244
abstract_inverted_index.agriculture	95
abstract_inverted_index.application	228
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abstract_inverted_index.introducing	112
abstract_inverted_index.rhizoctonia	5
abstract_inverted_index.validation,	145
abstract_inverted_index.Anthracnose,	0
abstract_inverted_index.YMV-affected	87
abstract_inverted_index.anthracnose,	82
abstract_inverted_index.disease-free	80
abstract_inverted_index.convolutional	72
abstract_inverted_index.environmental	243
abstract_inverted_index.pre-processed	119
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cited_by_percentile_year.min	96
countries_distinct_count	1
institutions_distinct_count	4
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citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	True