Prediction of Rice Disease from Leaves using Deep Convolution Neural Network towards a Digital Agricultural System

Al-Amin, Md.; Karim, Dewan Ziaul; Bushra, Tasfia Anika

doi:10.1109/iccit48885.2019.9038229

Cited by 37 publications

(7 citation statements)

References 8 publications

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“…In another study ( Al-Amin et al, 2019 ), researchers used a DCNN to identify late and early blight in potato harvests. Their research aimed to identify diseased potato leaves from healthy ones so that the infections may be detected early.…”

Section: Ai-based Automated Vegetables Disease Detection Classificationmentioning

confidence: 99%

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Jafar,

Bibi,

Naqvi

et al. 2024

Front. Plant Sci.

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Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.

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Section: Ai-based Automated Vegetables Disease Detection Classificationmentioning

confidence: 99%

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Jafar,

Bibi,

Naqvi

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

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“…Al-amin M. et al [7] used 900 images to classify four types of diseases. They applied gray scaling, blur removal and segmented the background for every image.…”

Section: Related Workmentioning

confidence: 99%

Rice Leaf Disease Recognition using Local Threshold Based Segmentation and Deep CNN

Islam¹,

Islam²,

Haque³

et al. 2021

IJISA

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Timely detection of rice diseases can help farmers to take necessary action and thus reducing the yield loss substantially. Automatic recognition of rice diseases from the rice leaf images using computer vision and machine learning can be beneficial over the manual method of disease recognition through visual inspection. During the recent years, deep learning, a very popular and efficient machine learning algorithm, has shown great promise in image classification task. In this paper, a segmentation-based method using deep neural network for classifying rice diseases from leaf images has been proposed. Disease-affected regions of the rice leaves have been segmented using local segmentation method and the Convolutional Neural Network (CNN) has been trained with those images. Proposed method has been applied on three different datasets including the one created by us which consists of the rice leaf images collected from Bangladesh Rice Research Institute (BRRI). Three state-of-the-art CNN architectures VGG, ResNet and DenseNet, used in the proposed method, have been trained with these three datasets for classifying the diseases. Classification performance of the proposed method using the said three CNN architectures for the three datasets have been analyzed and compared. These results show that this model is quite promising in classifying rice leaf diseases. Outcome of this research is an enhancement in the performance of rice disease classification which is quite significant for the viability of this work to be transformed into a real-time application for the farmers.

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“…Examples of texture characteristics are energy entropy, contrast, among others. According to the domain from which the texture feature is extracted, they can be classified into spatial texture feature extraction and spectral texture feature extraction (42,43) .…”

Section: Feature Extractionmentioning

confidence: 99%

Técnicas de procesamiento digital de imágenes para detección de plagas y enfermedades en cultivos: una revisión

Camperos

Jaramillo

Guerrero-Gómez

2021

inycomp

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La detección adecuada de plagas y enfermedades en la producción de cultivos es fundamental para aumentar la producción agrícola de forma sostenible. Es por esta razón, que se incorpora el término Agricultura 4.0, la cual integra un conjunto de tecnologías, dispositivos, protocolos y paradigmas computacionales para mejorar los procesos agrícolas. La información sobre las condiciones climáticas, suelos, enfermedades, insectos, semillas, fertilizantes, constituye una importante contribución al desarrollo económico y sostenible de este sector. Las técnicas de procesamiento digital de imágenes son una herramienta que permite la identificación temprana de las plagas o enfermedades en los cultivos como: cereales, frutales, raíces, hojas y tubérculos; y de esta forma, mitigar pérdidas económicas en el sector agrícola. A nivel mundial, alrededor del 40% de los cultivos son desechados por diversas enfermedades y plagas. En la mayoría de los casos, las enfermedades de los cultivos producen síntomas y características visibles durante el crecimiento de las plantas. Debido a la escasez de tecnologías utilizadas en los cultivos, el diagnóstico de las enfermedades y plagas se soporta en gran parte en la inspección humana, generando errores ocasionados por la subjetividad de los individuos. La presente revisión de literatura se llevó a cabo con la finalidad de identificar diferentes técnicas de procesamiento digital de imágenes para la prevención de plagas y enfermedades en cultivos de los diferentes sectores agrícolas. Los resultados demostraron que el sistema de diagnóstico está compuesto de la adquisición de imágenes, preprocesamiento de imágenes, la segmentación, la extracción de características, la selección de características y la posterior clasificación de las plagas o enfermedades; asimismo, se presentan las tendencias y desafíos actuales en la temática.

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Prediction of Rice Disease from Leaves using Deep Convolution Neural Network towards a Digital Agricultural System

Cited by 37 publications

References 8 publications

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Rice Leaf Disease Recognition using Local Threshold Based Segmentation and Deep CNN

Técnicas de procesamiento digital de imágenes para detección de plagas y enfermedades en cultivos: una revisión

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