Identification of plant disease infection using soft-computing: Application to modern botany

Kiani, Ehsan; Mamedov, Tofik

doi:10.1016/j.procs.2017.11.323

Cited by 47 publications

(14 citation statements)

References 10 publications

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“…Una de las ventajas que podemos observar cuando Derwin Suhartono et al (2013) [5] utilizó el algoritmo clasificador de lógica difusa, es lo fácil y rápido que pudo implementarlo, pues no requiere de una gran colección de datos para su entrenamiento, y le presentó resultados más o menos favorables al momento de querer clasificar . Ahora que, si se disminuyen la clase objetivo, pues tendría mejores resultados, como lo demuestra el trabajo de Kiani y Mamedov (2017) [12], que consiguió mejor precisión por solo el hecho de predecir solo una enfermedad.…”

Section: Discusión Y Resultadosunclassified

“…Los res ultados que obtienen son alentadores, ya que alcanzaron una optimización del 97% en la segmentación y clasificación de las enfermedades con un tiempo de procesamiento de 1.2 segundos. Con este tipo de técnicas comprueban que es factible de ser implantadas en un circuito integrado, adaptado a un robot con visión artificial y que pudiera hacer una inspección automatizada en un centro botánico moderno [12].…”

Section: Revisión De La Literaturaunclassified

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Detección de enfermedades en el sector agrícola utilizando Inteligencia Artificial

Ortega¹,

Biswal²,

Sánchez-DelaCruz³

2019

RCS

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El presente artículo, expone varios trabajos de investigación sobre la posibilidad de detectar enfermedades en los cultivos agrícolas, utilizando técnicas de inteligencia artificial. Los autores de dichos trabajos, hacen propuestas de técnicas de obtención de características de las hojas o frutos de las plantas, así como también el uso de algoritmos clasificadores o de agrupación, todo esto con el fin de determinar si una hoja, presenta signos de alguna enfermedad. Al haber diversos tipos de enfermedades y diversas variedades de plantas, los autores hacen propuestas para utilizar el algoritmo que ellos consideran el que obtendrá mejores resultados. Al final concluimos que, sí es posible detectar enfermedades trayendo consigo un beneficio directo para el agricultor que la implemente, ya que un diagnóstico oportuno, daría una respuesta a la enfermedad y por lo tanto reducción del riesgo en pérdidas económicas.

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Section: Discusión Y Resultadosunclassified

Section: Revisión De La Literaturaunclassified

Detección de enfermedades en el sector agrícola utilizando Inteligencia Artificial

Ortega¹,

Biswal²,

Sánchez-DelaCruz³

2019

RCS

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“…Fuzzy logic-based classification algorithms can accurately replicate the capabilities of a farmer's brain to classify the infected crops. It has been tested successfully to differentiate between healthy and iron deficient or infected strawberry leaves [159]. Hyperspectral imaging techniques have also been accepted by researchers for the diagnosis of plant diseases.…”

Section: Detection Of Plant Diseasesmentioning

confidence: 99%

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Hati

Singh

2021

AgriEngineering

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Conventional farming necessitates a large number of resources and infrastructure such as land, irrigation, manpower to manage farms, etc. Modern initiatives are required to automate conventional farms. Smart indoor farms offer the potential to remedy the shortfalls of conventional farms by providing a controlled, intelligent, and smart environment. This paper presents a three-dimensional perspective consisting of soilless farming, energy harvesting, and smart technologies, which could be considered as the three important characteristics of smart indoor farms. A six-layer smart indoor farms architecture has also been proposed, which explains how data are collected using various sensors and devices and then transmitted onto the cloud infrastructure for further analysis and control through various layers. Artificial lighting, smart nutrition management, and artificial climate control, to name a few, are some of the important requirements for smart indoor farms while considering control and service management factors. The major bottleneck in installing such systems is both the economical and the technical constraints. However, with the evolution of technology (and when they become widely available in the near future), a more favourable farming scenario may emerge. Furthermore, smart indoor farms could be viewed as a potential answer for meeting the demands of a sustainable agricultural revolution as we move closer to Agriculture 4.0. Finally, in order to adapt smart indoor farms and their study scope, our work has presented various research areas to potential researchers.

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“…Tetila et al compared the performance of different classifiers, including sequential minimal optimization (SMO), adaboost, decision trees, K-NN, random forest, and naive Bayes, for the identification of soybean foliar diseases [ 5 ]. Ehsan et al proposed a fuzzy logic classification algorithm to improve classification efficiency for healthy and disease infected strawberry leaves [ 6 ]. When these classical machine learning methods such as random forest, adaboost, decision trees and support vector machine are used to identify plant diseases, it is necessary to extract plant disease features which has a great influence on the identification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Few-shot cotton leaf spots disease classification based on metric learning

Liang

2021

Plant Methods

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Background Cotton diceases seriously affect the yield and quality of cotton. The type of pest or disease suffered by cotton can be determined by the disease spots on the cotton leaves. This paper presents a few-shot learning framework that can be used for cotton leaf disease spot classification task. This can be used in preventing and controlling cotton diseases timely. First, disease spots on cotton leaf’s disease images are segmented by different methods, compared by using support vector machine (SVM) method and threshold segmentation, and discussed the suitable one. Then, with segmented disease spot images as input, a disease spot dataset is established, and the cotton leaf disease spots were classified using a classical convolutional neural network classifier, the structure and framework of convolutional neural network had been designed. At last, the features of two different images are extracted by a parallel two-way convolutional neural network with weight sharing. Then, the network uses a loss function to learn the metric space, in which similar leaf samples are close to each other and different leaf samples are far away from each other. In summary, this work can be regarded as a significang reference and the benchmark comparison for the follow-up studies of few-shot learning tasks in the agricultural field. Results To achieve the classification of cotton leaf spots by small sample learning, a metric-based learning method was developed to extract cotton leaf spot features and classify the sick leaves. The threshold segmentation and SVM were compared in the extracting of leaf spot. The results showed that both of these two method can extract the leaf spot in a good performance, SVM expented more time, but the leaf spot which extracted from SVM was much more suitable for classifying, thus SVM method can retain much more information of leaf spot, such as color, shape, textures, ect, which can help classficating the leaf spot. In the process of leaf spot classification, the two-way parallel convolutional neural network was established for building the leaf spot feature extractor, and feature classifier is constructed. After establishing the metric space, KNN was used as the spot classifier, and for the construction of convolutional neural networks, commonly used models were selected for comparison, and a spatial structure optimizer (SSO) is introduced for local optimization of the model, include Vgg, DesenNet, and ResNet. Experimentally, it is demonstrated that the classification accuracy of DenseNet is the highest, compared to the other two networks, and the classification accuracy of S-DenseNet is 7.7% higher then DenseNet on average for different number of steps. Conclusions As the step increasing, the accuracy of DesenNet, and ResNet are all improved, and after using SSO, each of these neural networks can achieved better performance. But The extent of increase varies, DesenNet with SSO had been improved the most obviously.

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Identification of plant disease infection using soft-computing: Application to modern botany

Cited by 47 publications

References 10 publications

Detección de enfermedades en el sector agrícola utilizando Inteligencia Artificial

Detección de enfermedades en el sector agrícola utilizando Inteligencia Artificial

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Few-shot cotton leaf spots disease classification based on metric learning

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