Recent Studies of Image and Soft Computing Techniques for Plant Disease Recognition and Classification

Sabrol, Hiteshwari; Kumar, Satish

doi:10.5120/ijca2015905982

Cited by 23 publications

(7 citation statements)

References 38 publications

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“…In the early studies on plant diseases and pests classification methods based on deep learning, many researchers took advantage of the powerful feature extraction capability of CNN, and the methods were combined with traditional classifiers [ 38 ]. First, the images are input into a pretrained CNN network to obtain image characterization features, and the acquired features are then input into a conventional machine learning classifier (e.g., SVM) for classification.…”

Section: Plant Diseases and Pests Detection Methods Based On Deep Leamentioning

confidence: 99%

Plant diseases and pests detection based on deep learning: a review

2021

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Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.

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Section: Plant Diseases and Pests Detection Methods Based On Deep Leamentioning

confidence: 99%

Plant diseases and pests detection based on deep learning: a review

2021

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“…This study used different steps in the classification of eggplant diseases which was adopted from the study of Sabrol, et al [32]. The first step is image acquisition, followed by pre-processing of acquired images such as cropping, resizing, and augmentation, the third step is feature extraction and the last step is image classification.…”

Section: Methodsmentioning

confidence: 99%

“…Sabrol, et al [32] used different methods before the recognition and classification of plant diseases. They acquired images using digital devices which is similar with the study of Singh, et al [33] and they conducted image pre-processing techniques such as smoothing, enhancement and filtering.…”

Section: Related Workmentioning

confidence: 99%

Mobile-Based Eggplant Diseases Recognition System using Image Processing Techniques

Maggay¹

2020

IJATCSE

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Agricultural inputs are very important; however, these are not always available to farmers. This study aimed to develop a mobile-based eggplant diseases recognition system using image processing techniques to provide information to the farmers with regard to the identification of eggplant diseases. Quantitative method using descriptive survey was used to gather data. Weighted mean was also used to analyze the rating on the extent of compliance of the system along with ISO/IEC 25010 Software Quality Standards. Convenience sampling was employed in selecting the participants. The study employed Scrum Methodology which takes an iterative approach to software development. It also utilized different image processing techniques and adapted the MobilenetV2 framework. The study found out that farmers encountered issues such as lack of modern agricultural inputs, capital, labor force, price fluctuations, production costs, water system, and unsupervised use of chemicals. They also encountered challenges such as the presence of insect pests and diseases, natural disaster, drought, and limited access to production inputs and equipment. Result of the system evaluation using ISO/IEC 25010 Software Quality Standards shows that the system is compliant to a great extent with overall weighted mean of 4.12. Based on the findings of the study, the developed mobile application can be of great help to the farmers in identifying the different eggplant pests and diseases as well as in providing the necessary management and control.

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“…Many more such image processing operations are available, that may be used as image pre-processing operation. [2,3,4]. The process of detecting crop illnesses using DIP and ML techniques is divided into the following modules: picture capture, image pre-processing, image segmentation, feature extraction, and identification or classification.…”

Section: A Detection Of Plant Diseases Using Image Processingmentioning

confidence: 99%

Image Processing based Plant Disease Detection and Classification

Uppal¹,

Naruka²,

Tewari

2023

IJRITCC

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Generally, it has been observed that due to lack of proper knowledge of disease intensity, the farmer is not able to use the pesticide in proper quantity to treat the diseases. The use of pesticide mostly becomes more than necessary, due to which there is not only a loss of money, but also it causes soil and environmental pollution. If diseases severity-wise labelled data sets are available, it can be used to develop pesticide recommendation systems. Images with least infection severity can be used to train and validate a DL model to capture the plant diseases at very initial stage. Classification techniques may be viewed as variations of detection systems; however, instead of attempting to identify only one particular illness among several diseases, classification methods detect and name the diseases harming the plant. This presents various classification and plant disease detection methods based on image processing with results.

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Recent Studies of Image and Soft Computing Techniques for Plant Disease Recognition and Classification

Cited by 23 publications

References 38 publications

Plant diseases and pests detection based on deep learning: a review

Plant diseases and pests detection based on deep learning: a review

Mobile-Based Eggplant Diseases Recognition System using Image Processing Techniques

Image Processing based Plant Disease Detection and Classification

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