Plant diseases recognition for smart farming using model-based statistical features

Hlaing, Chit Su; Zaw, Sai Maung Maung

doi:10.1109/gcce.2017.8229343

Cited by 21 publications

(8 citation statements)

References 4 publications

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“…SIFT transforms the grayscale image into scale‐invariant features that are invariant to image scale, rotation, illumination, and changing viewpoints 51 . It detects key points and computes description feature vectors for each key point.…”

Section: Methodsmentioning

confidence: 99%

Integrated analysis of machine learning and deep learning in chili pest and disease identification

2020

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BACKGROUND Chili is one of the most important and high‐value vegetable crops worldwide. However, pest and disease infections are among the main limiting factors in chili cultivation. These diseases cannot be eradicated but can be handled and monitored to mitigate the damage. Hence, the use of an automated identification system based on images will promote quick identification of chili disease. The features extracted from the images are of utmost importance to develop such an accurate identification system. RESULTS In this research, chili pest and disease features extracted using the traditional approach were compared with features extracted using a deep‐learning‐based approach. A total of 974 chili leaf images were collected, which consisted of five types of diseases, two types of pest infestations, and a healthy type. Six traditional feature‐based approaches and six deep‐learning feature‐based approaches were used to extract significant pests and disease features from the chili leaf images. The extracted features were fed into three machine learning classifiers, namely a support vector machine (SVM), a random forest (RF), and an artificial neural network (ANN) for the identification task. The results showed that deep learning feature‐based approaches performed better than the traditional feature‐based approaches. The best accuracy of 92.10% was obtained with the SVM classifier. CONCLUSION A deep‐learning feature‐based approach could capture the details and characteristics between different types of chili pests and diseases even though they possessed similar visual patterns and symptoms. © 2020 Society of Chemical Industry

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Section: Methodsmentioning

confidence: 99%

Integrated analysis of machine learning and deep learning in chili pest and disease identification

2020

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“…However, the proposed method fails to identify apple leaf diseases under natural illumination. In [ 68 ], a method for plant disease detection based on SIFT features is proposed. The input image is pre-processed to extract the whole region of the leaf.…”

Section: Plant/fruit Health Protection and Disease Detection Approachesmentioning

confidence: 99%

Machine Vision Systems in Precision Agriculture for Crop Farming

et al. 2019

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Machine vision for precision agriculture has attracted considerable research interest in recent years. The aim of this paper is to review the most recent work in the application of machine vision to agriculture, mainly for crop farming. This study can serve as a research guide for the researcher and practitioner alike in applying cognitive technology to agriculture. Studies of different agricultural activities that support crop harvesting are reviewed, such as fruit grading, fruit counting, and yield estimation. Moreover, plant health monitoring approaches are addressed, including weed, insect, and disease detection. Finally, recent research efforts considering vehicle guidance systems and agricultural harvesting robots are also reviewed.

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“…A SmartAgriFood conceptual architecture is proposed in Kaloxylos et al [22], while the authors of [23] introduce internet applications in the agri-food domain; Poppe in [24] proposes the analysis to both the scope and the organization of farm production regulations. Garba [25] develops smart water-sharing methods in semi-arid regions; Hlaing et al [26] introduce plant diseases recognition using statistical models; and, moreover, in Alipio et al [27], there are smart hydroponics systems that exploit inference in Bayesian networks. Marimuthu et al [28] propose and design a Persuasive Technology to encourage smart farming, while also exploiting historical time-series for production quality assurance [29], because nowadays consumers are concerned about food safety assurance related to health and well-being.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

2018

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This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it.

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Plant diseases recognition for smart farming using model-based statistical features

Cited by 21 publications

References 4 publications

Integrated analysis of machine learning and deep learning in chili pest and disease identification

Integrated analysis of machine learning and deep learning in chili pest and disease identification

Machine Vision Systems in Precision Agriculture for Crop Farming

Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

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