Recent machine learning based approaches for disease detection and classification of agricultural products

Tripathi, Mukesh Kumar; Maktedar, Dhananjay D.

doi:10.1109/iccubea.2016.7860043

Cited by 50 publications

(16 citation statements)

References 10 publications

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“…Paper [15] presents popular methods based on machine learning algorithms such as Kmeans, NN, CCM to detection and classify agricultural products disease.…”

Section: Literature Reviewmentioning

confidence: 99%

Plant Diseases Detection and Classification Based on Image Processing

2020

IJEAT

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To further the progress of sustainable development and solve real-life problems we have seen many processes are applied in our life, like artificial intelligence and decisionmaking. Morocco is one of the countries that rely heavily on agriculture and food production. So, food production is considered the basic needs of a human being for that we have seen fast advancements in agriculture productivity to meet the projected demand. However, with the time passing by, all species of plants are subjected to various types of diseases that cause huge damage. Although the observation of variation in the infected part of the leaf plant is very important but not enough because the perception of the human eye is not so much stronger. The identification of plant diseases is a very important task in the agriculture area. So, the best identification means there is a huge gain on agricultural productivity, quality, and quantity. To detect plant diseases in an earlier stage we require efficient and precise techniques to assist farmers in decision-making. This article presents, first, an overview of plant diseases from leaves images and different disease classification approaches that can be used for plant leaf disease detection.

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“…Paper [15] presents popular methods based on machine learning algorithms such as Kmeans, NN, CCM to detection and classify agricultural products disease.…”

Section: Literature Reviewmentioning

confidence: 99%

Plant Diseases Detection and Classification Based on Image Processing

2020

IJEAT

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“…The methodology includes image acquisition, image preprocessing, feature extraction with Gray level co-occurrence matrix (GLCM) and finally classified with two types: Unsupervised classification and supervised classification. In paper [9] popular methods have been utilizes machine learning, image processing and classification based approaches to identify and detect the disease of agricultural product.…”

Section: Sift Deskmentioning

confidence: 99%

“…Machine based approaches for disease detection and classification of agricultural product have become an important part of civilization. Paper [9] presents a review on existing reported techniques useful in detection of disease.…”

Section: Sift Desk Introductionmentioning

confidence: 99%

Plant Disease Detection by Image Processing: A Literature Review

Halder¹,

Sarkar²,

Bahar³

2018

SDRP-JFST

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To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the Internet-of-Things (IOT). These processes above are related for solving real life problems. Food is one of the basic needs of human being. World population is increasing day by day. So it has become important to grow sufficient amount of crops to feed such a huge population. But with the time passing by, plants are affected with various kinds of diseases, which cause great harm to the agricultural plant productions. Beside that many countries economy greatly depends on agricultural productivity and it's also a need for a coun try to attain agricultural productivity of basic agricultural product for the people of that particular country. Detection of plant disease through some automatic technique is beneficial as it requires a large amount of work of monitoring in big farm of crops, and at very early stage itself it detects symptoms of diseases means where they appear on plant leaves. In this paper surveys on different disease classification techniques that can be used for plant leaf disease detection.

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“…Several computer systems have been developed for improving agriculture. For example, plant disease detection, [12][13][14][15] quality inspection of agriculture products, 16 and vegetable classification. 17 These systems were mainly developed based on standard computer vision and machine learning methods such as support vector machine (SVM).…”

Section: Research Motivationmentioning

confidence: 99%

Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles

Moon

Kwak

et al. 2017

J. Appl. Remote Sens

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, "Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles," J. Appl. Remote Sens. 11(4), 042621 (2017), doi: 10.1117/1.JRS.11.042621. Abstract. Recently, unmanned aerial vehicles (UAVs) have gained much attention. In particular, there is a growing interest in utilizing UAVs for agricultural applications such as crop monitoring and management. We propose a computerized system that is capable of detecting Fusarium wilt of radish with high accuracy. The system adopts computer vision and machine learning techniques, including deep learning, to process the images captured by UAVs at low altitudes and to identify the infected radish. The whole radish field is first segmented into three distinctive regions (radish, bare ground, and mulching film) via a softmax classifier and K-means clustering. Then, the identified radish regions are further classified into healthy radish and Fusarium wilt of radish using a deep convolutional neural network (CNN). In identifying radish, bare ground, and mulching film from a radish field, we achieved an accuracy of ≥97.4%. In detecting Fusarium wilt of radish, the CNN obtained an accuracy of 93.3%. It also outperformed the standard machine learning algorithm, obtaining 82.9% accuracy. Therefore, UAVs equipped with computational techniques are promising tools for improving the quality and efficiency of agriculture today.

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Recent machine learning based approaches for disease detection and classification of agricultural products

Cited by 50 publications

References 10 publications

Plant Diseases Detection and Classification Based on Image Processing

Plant Diseases Detection and Classification Based on Image Processing

Plant Disease Detection by Image Processing: A Literature Review

Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles

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