scholarly journals Deep Learning for Leaf Disease Detection using CNN

2021 ◽  
Vol 9 (VI) ◽  
pp. 3279-3283
Author(s):  
Abhinay Kumar Reddy
Keyword(s):  
Image Processing ◽  
Learning Strategies ◽  
Computer Literacy ◽  
Crop Production ◽  
Black People ◽  
Plant Diseases ◽  
The Public ◽  
Saliency Maps ◽  
The Many ◽  
Processing Techniques

Agriculture is the backbone of the Indian economy. About 70% of people rely on it and share a large portion of GDP. Diseases in plants especially in the leaves affect the reduction of both quality and quantity of agricultural products. The human eye is not so powerful to detect minute differences in the infected part of the leaf. In this paper, we offer a software solution to automatically detect and diagnose plant leaf diseases. In this we use image processing techniques for the diagnosis and early diagnosis can be done as elsewhere. This approach will improve crop production. It involves several steps. Picture detection, pre-image processing, percentage separation, extraction and neural features. Recently, many researchers have advocated since the success of in-depth computer literacy the idea of improving the effectiveness of diagnostic programs. Unfortunately, most of these studies did not use the latest in-depth formats and were based on AlexNet, GoogleNet or similar properties. Moreover, deep-seated mechanisms do not exist to take advantage of it, making these deep divisions invisible and suitable as black people boxes. In this project, we tested the many technological approaches of the Convolutional Neural Network (CNN) buildings using various learning strategies in the public database of plant diseases separation. These new structures exceed the high-quality effects of plant diseases in stages with very high accuracy. In addition, we have suggested the use of saliency maps as a way to visualize and interpret CNN classification.

SOYBEAN LEAF DISEASES DETECTION AND CLASSIFICATION USING RECENT IMAGE PROCESSING TECHNIQUES

2020 ◽  
Vol 8 (3) ◽  
pp. 01-08
Author(s):  
Arpan Singh Rajput ◽  
Shailja Shukla ◽  
S. S. Thakur
Keyword(s):  
Image Processing ◽  
Early Stage ◽  
Weather Condition ◽  
Plant Diseases ◽  
Soybean Plant ◽  
Plant Leaf ◽  
Image Processing Techniques ◽  
Soybean Leaf ◽  
Processing Techniques ◽  
Soybean Diseases

Purpose: India is an agricultural country and soybean production is one of the major sources of earning. Due to the major factors like diseases, pest attacks, and sudden changes in the weather condition, the productivity of the soybean crop decreases. Automatic detection of soybean plant diseases is essential to detect the symptoms of soybean diseases as early as they appear on the growing stage. This paper proposed a methodology for the analysis and detection of soybean plant leaf diseases using recent digital image processing techniques. In this paper, experimental results demonstrate that the proposed method can successfully detect and classify the major soybean diseases. Methodology: MatLab 18a is used for the simulation for the result and machine learning-based recent image processing techniques for the detection of the soybean leaf disease. Main Findings: The main finding of this work is to create the soybean leaf database which includes healthy and unhealthy leaves and achieved 96 percent accuracy in this work using the proposed methodology. Applications of this study: To detect soybean plant leaf diseases in the early stage in Agricultural. The novelty of this study: Self-prepared database of healthy and unhealthy images of soybean leaf with the proposed algorithm.

scholarly journals A Review On Leaves Disease Segmentation And Classification Techniques

2019 ◽  
pp. 132-140
Author(s):  
Soumya Ranjan Sahu ◽  
Chandra Sekhar Panda
Keyword(s):  
Image Processing ◽  
Human Health ◽  
Traditional Method ◽  
Plant Diseases ◽  
Image Processing Techniques ◽  
Naked Eye ◽  
The People ◽  
Different Types ◽  
Processing Techniques

Agriculture plays a major role in our society. Most of the people depend on agriculture for their living. It becomes very important part of society for their livelihood. But there are some problems on agriculture that directly or indirectly affect the human health and also economy. The major problem for agriculture is the plant diseases. This paper is based on a survey of different types of techniques used for segmenting and classification of plant diseases by using image processing techniques. By these techniques, we can easily detect the area of the infected part or can identify the type of disease. This paper gives various techniques used by various authors to detect the disease fast an accurately. They used different types of segmentation techniques like region based, clustering, thresolding etc. to detect the infected part of the leaves and by using the classifier they classify the disease name. The traditional method of naked eye observation can be overcome by introducing these methods. Main focus of our work is to analysis of fast and accurate techniques to identify the plant diseases.

Image Processing Technique for the Detection of Alberseem Leaves Diseases Based on Soft Computing

2021 ◽  
pp. 103-115
Author(s):  
Eimad Abdu Abusham
Keyword(s):  
Image Processing ◽  
Traditional Method ◽  
Processing Technique ◽  
Plant Diseases ◽  
Image Processing Technique ◽  
Economic Losses ◽  
Image Processing Techniques ◽  
Appropriate Treatment ◽  
Processing Techniques

Detecting plant diseases using the traditional method such as the naked eye can sometimes lead to incorrect identification and classification of the diseases. Consequently, this traditional method can strongly contribute to the losses of the crop. Image processing techniques have been used as an approach to detect and classify plant diseases. This study aims to focus on the diseases affecting the leaves of al-berseem and how to use image processing techniques to detect al-berseem diseases. Early detection of diseases important for finding appropriate treatment quickly and avoid economic losses. Detect the plant disease is based on the symptoms and signs that appear on the leaves. The detection steps include image preprocessing, segmentation, and identification. The image noise is removed in the preprocessing stage by using the MATLAB features energy, mean, homogeneity, and others. The k-mean-clustering is used to detect the affected area in leaves. Finally, KNN will be used to recognize unhealthy leaves and determines disease types (fungal diseases, pest diseases (shall), leaf minor (red spider), and deficiency of nutrient (yellow leaf)); these four types of diseases will detect in this thesis. Identification is the last step in which the disease will identify and classified.

scholarly journals Plant Disease Prediction System using Image Processing

2020 ◽  
pp. 162-167
Author(s):  
B Uma Jagadeswari ◽  
D Harshitha ◽  
G Vineela ◽  
B Siri ◽  
Yaragani Sowmya
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Negative Impact ◽  
Agricultural Productivity ◽  
Significant Loss ◽  
Plant Diseases ◽  
Disease Prediction ◽  
Agricultural Field ◽  
Processing Techniques ◽  
Leaf Image

Agriculture productivity is the major issue which affects the Indian economy. Crop cultivation plays an essential role in the agricultural field. Presently, the loss of food is mainly due to infected crops, which reflexively reduces the production rate. The major cause for decrease in the quality and amount of agricultural productivity is due to the diseases in plants. The occurrence of diseases in plants may result in significant loss in both quality and quantity of agricultural productivity. This can produce the negative impact on the countries whose economies are primarily dependent on the agriculture. Farmers encounter great difficulties in detecting and controlling plant diseases. Hence the detection of plant diseases in the earlier stages is very important to avoid the loss in terms of quality, quantity and finance. This paper mainly focuses on the approach based on image processing techniques that help farmers for detecting the diseases of plants by uploading leaf image to the system.

scholarly journals Agriculture Crop Leaf Disease Detection using Image Processing

2021 ◽  
Vol 10 (7) ◽  
pp. 110-114
Author(s):  
Venkatesh T. ◽  
Prathyush K. ◽  
Deepak* S. ◽  
U.V.S.A.M. Preetham
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Plant Diseases ◽  
Huge Amount ◽  
Or Efficiency ◽  
Plant Leaf ◽  
Image Processing Techniques ◽  
The One ◽  
Processing Techniques

As we all know that the Agriculture plays an important role in the Indian economy and majority of the individuals depends upon it and offers huge amount of the crops through the worldwide. The Illnesses in these crops are generally on the leaf's influences on the decrease of both quality and number of horticultural items. We should know the disease of the crop correctly to solve the problem. There will be a huge loss if we do not find the disease and treat properly. The view of natural eye isn't so a lot more grounded in order to watch minutevariety in the contaminated piece of leaf. In thisreport, we are giving a programming answer fornaturally identify and arrange plant leaf diseases. In this we are utilizing picture preparing methods to characterize alignments and rapidly finding can be completed according to infection. This methodology will upgrade the efficiency of yields in a efficient way and can get us the accurate disease which helps us to find the solution for the diseased crop. It observes a few stages with the help of these pictures obtaining, picture pre-handling, division, highlights extraction and genetic algorithm-based grouping. Relating to the cultivation of land, efficiency is something on which economy exceptionally depends. This is the one of the reasons that sickness identification in plants assumes a significant job in the agriculture business field, as having the illness in plants are very normal. In an event that legitimate consideration isn't taken here, at that point it causes true consequences for plantsand because of which quality of each and every item, amount or efficiency is being influenced. The recognition of plant infections through some programmed step is gainful as it avoids a huge work of checking in huge homesteads of harvests. At the beginning of the crop harvesting step itself, it shows the side effects or the symptoms of the diseases. This proposed method surfaces into a new programmed manner by distinguishing the effects of the crop plant diseases. We are using some image processing techniques for the identification of the disease. Additionally, it watches the review on the various diseases order strategies which also can be utilized for plant leaf alignment. Picture division, which is a significant viewpoint for sickness identificationin a plant leaf alignment, is finalized by the input RGB mask images.

scholarly journals Diagnosis of Leaf Surface Disease Using Two Datasets of Tomato and Rice Obtained from Image Processing Techniques

2021 ◽  
Vol 8 (3) ◽  
pp. 77-85
Author(s):  
Seiyedeh Khadijeh Hosseiny ◽  
Nasersadeghi Jola ◽  
Seiyedeh Maryam Hosseiny
Keyword(s):  
Image Processing ◽  
Leaf Surface ◽  
Accurate Method ◽  
Plant Diseases ◽  
Improved Genetic Algorithm ◽  
Data Set ◽  
Image Processing Techniques ◽  
Modern Agriculture ◽  
Selection Step ◽  
Processing Techniques

It is of a great importance in modern agriculture to study fast, automatic, inexpensive and accurate method of diagnosing plant diseasesTherefore, timely and accurately diagnosis of the disease in the fields is one of the most important factors in dealing with plant diseases. For this reason, in the present study, the image processing method study, has been examined for diagnosing the two important diseases of rice and tomato, brown spots and leaf blasts. In this study, firstly the data section is treated using improved k-means segmentation, after preprocessing. Secondly, comprehensive features are extracted and the disease areas are demarcated. An improved genetic algorithm is used in the feature selection step. Finally, images are categorized using the k-nearest neighbor’s algorithm (k-NN) classifier. The accuracy of the proposed method for the rice data set is 99.12 and for the tomato data set is 97.29, which shows a very good performance compared to other methods.

scholarly journals Monitoring of Sona Massori Paddy Crop and its Pests Using Image Processing

2017 ◽  
Vol 6 (02) ◽  
pp. 01-06
Author(s):  
Prof. Barry Wiling
Keyword(s):  
Image Processing ◽  
South India ◽  
Processing Technique ◽  
Plant Diseases ◽  
Image Processing Technique ◽  
Morphological Operations ◽  
Pest Monitoring ◽  
Paddy Crop ◽  
Processing Techniques ◽  
Crop Health

Nowadays plant diseases are the major cause of low agriculture yield.  So significance of detecting diseases in early stages and treating it will improve the agriculture yield. In India the major agriculture crop is paddy and in central part of south India there is a specific paddy crop called Sona Massori. In our work we concentrated on Sona Masori paddy crop health and pest monitoring using image processing. Here image processing technique is used to observe the image of the leaf and based on the image the diseases are identified using the following process such as image acquisition, pre-processing, segmentation, feature extraction and classification. Mostly diseases are caused by pests, insects and pathogens. In Sona Massori paddy crop 99% of diseases are caused by pests, So detecting pests also plays an important role in improving yield. Here pests are detected by image processing techniques such as Gaussian blur and morphological operations.

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