Classification of macronutrient deficiencies in maize plants using optimized multi class support vector machines

Leena, N.; Saju, K. K.

doi:10.1016/j.eaef.2018.11.002

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…Figure 2 shows maize disease images classification using machine learning. HSV, noise filtering [24], [25], HSI [26] and noise removal [27].…”

Section: Answer the First Research Questionmentioning

confidence: 99%

“…Classification is a step for grouping features based on similarity or proximity. Various classical machine learning methods are used for classification such as Naïve Bayes [4], [30], [32], Decision Tree [4], [27], [30], [32], k-Nearest Neighbor [4], [33], Support Vector Machine with all its variants [4]- [6], [23], [25], [26], [29]- [35], Random Forest [4], [30], [32], Deep Forest [4], [36]. Neural Network [22], [24], [25], [32], and Bag of Features [6].…”

Section: Classificationmentioning

confidence: 99%

“…Preprocessing aims to enhance image quality. Preprocessing on maize classification articles include grayscale, resize [4]-[6], histogram equalization [22], LAB Color [23],HSV, noise filtering[24],[25], HSI[26] and noise removal[27].…”

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confidence: 99%

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Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Setiawan

Rochman

Satoto

et al. 2022

J. Phys.: Conf. Ser.

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Image classification of maize disease is an agriculture computer vision application. In general, the application of computer vision uses two methods: machine learning and deep learning. Implementations of machine learning classification cannot stand alone. It needs image processing techniques such as preprocessing, feature extraction, and segmentation. Usually, the features are selected manually. The classification uses k-nearest neighbor, naïve bayes, decision tree, random forest, and support vector machine. On the other side, deep learning is part of machine learning. It is a development of an artificial neural network that performs automatic feature extraction. Deep learning is capable of recognizing large data but requires high-speed computation. This article compare machine learning and deep learning for maize leaf disease classification. There are five research questions: how to get data, how machine learning and deep learning classify images, how the classification result compare both of them and the opportunities & challenges of research on maize leaf disease classification. The number of articles to review was 62, consisting of 18 articles using machine learning, 28 articles applying deep learning, and the rest are supporting articles.

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“…Figure 2 shows maize disease images classification using machine learning. HSV, noise filtering [24], [25], HSI [26] and noise removal [27].…”

Section: Answer the First Research Questionmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Setiawan

Rochman

Satoto

et al. 2022

J. Phys.: Conf. Ser.

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“…Classification models based on SVM are widely used in the literature to perform the predictive analyses on data from several problems and fields. Only in the last two years, SVM has been successfully employed to solve problems in domains, such as geology (García-Nieto et al, 2019;Huang et al, 2017;Jung et al, 2018;Kumar et al, 2017;Mahvash and Hezarkhani, 2018;Pu et al, 2019), hydrological sciences (Choubin et al, 2019b(Choubin et al, , 2018Kisi et al, 2019;Rahmati et al, 2019;Sajedi-Hosseini et al, 2018), climate and weather (Fan et al, 2018;Kundu et al, 2017;Yu et al, 2018Yu et al, , 2017, fault detection in various systems and processes (Ali et al, 2018;Fazai et al, 2019;Ghalyani and Mazinan, 2019;Han et al, 2019;Liu and Zio, 2018;Manjurul Islam and Kim, 2019;Saari et al, 2019;Xi et al, 2019), health and medicine (Battineni et al, 2019;Di et al, 2019;Liu et al, 2019;Lukmanto et al, 2019;Vougas et al, 2019), agriculture (Akbarzadeh et al, 2018;Feng et al, 2019;Fernandes et al, 2019;Griffel et al, 2018;Leena and Saju, 2019;Radhakrishnan and Ramanathan, 2018;Zhou et al, 2019) power and energy systems (Ma et al, 2018;…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Determining the geographical origin of lettuce with data mining applied to micronutrients and soil properties

Maione

Araújo

Santos-Araujo

et al. 2022

Sci. agric. (Piracicaba, Braz.)

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Lettuce (Lactuca sativa) is the main leafy vegetable produced in Brazil. Since its production is widespread all over the country, lettuce traceability and quality assurance is hampered. In this study, we propose a new method to identify the geographical origin of Brazilian lettuce. The method uses a powerful data mining technique called support vector machines (SVM) applied to elemental composition and soil properties of samples analyzed. We investigated lettuce produced in São Paulo and Pernambuco, two states in the southeastern and northeastern regions in Brazil, respectively. We investigated efficiency of the SVM model by comparing its results with those achieved by traditional linear discriminant analysis (LDA). The SVM models outperformed the LDA models in the two scenarios investigated, achieving an average of 98 % prediction accuracy to discriminate lettuce from both states. A feature evaluation formula, called F-score, was used to measure the discriminative power of the variables analyzed. The soil exchangeable cation capacity, soil contents of low crystalized Al and Zn content in lettuce samples were the most relevant components for differentiation. Our results reinforce the potential of data mining and machine learning techniques to support traceability strategies and authentication of leafy vegetables.

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“…However, generating solutions for agricultural production is a complex task that requires the consideration of several variables. One critical variable is the nutritional status of crops, which is determined by approximately 14 fundamental nutrients that plants require for their growth [3]. Each of these nutrients is found in specific amounts and plays essential roles in crop metabolism.…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning for Image Classification of Phosphorus Nutrition States in Individual Maize Leaves

Ramos-Ospina,

Gomez,

Trujillo

et al. 2023

Electronics

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Computer vision is a powerful technology that has enabled solutions in various fields by analyzing visual attributes of images. One field that has taken advantage of computer vision is agricultural automation, which promotes high-quality crop production. The nutritional status of a crop is a crucial factor for determining its productivity. This status is mediated by approximately 14 chemical elements acquired by the plant, and their determination plays a pivotal role in farm management. To address the timely identification of nutritional disorders, this study focuses on the classification of three levels of phosphorus deficiencies through individual leaf analysis. The methodological steps include: (1) using different capture devices to generate a database of images composed of laboratory-grown maize plants that were induced to either total phosphorus deficiency, medium deficiency, or total nutrition; (2) processing the images with state-of-the-art transfer learning architectures (i.e., VGG16, ResNet50, GoogLeNet, DenseNet201, and MobileNetV2); and (3) evaluating the classification performance of the models using the created database. The results show that the DenseNet201 model achieves superior performance, with 96% classification accuracy. However, the other studied architectures also demonstrate competitive performance and are considered state-of-the-art automatic leaf nutrition deficiency detection tools. The proposed method can be a starting point to fine-tune machine-vision-based solutions tailored for real-time monitoring of crop nutritional status.

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Classification of macronutrient deficiencies in maize plants using optimized multi class support vector machines

Cited by 10 publications

References 10 publications

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Determining the geographical origin of lettuce with data mining applied to micronutrients and soil properties

Deep Transfer Learning for Image Classification of Phosphorus Nutrition States in Individual Maize Leaves

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