scholarly journals Prediction and Analysis of Paddy Crops Disease in Artificial Intelligence Techniques

2021 ◽  
pp. 261-268
Author(s):  
Tanmayee Tushar Parbat ◽  
Rohan Benhal ◽  
Honey Jain ◽  
Dr. Vinayak Musale
Keyword(s):  
Image Processing ◽  
Early Stage ◽  
Processing Technique ◽  
Plant Diseases ◽  
Image Processing Technique ◽  
Human Beings ◽  
Yield Level ◽  
Disease Occurrence ◽  
Causative Agents ◽  
Paddy Crop

The survival of human beings is generally based on the proper productivity of agriculture. The paddy plant is considered as a major planting crop in improving the economical level of our country. Nowadays, the yield level of paddy crop might be minimized due to several diseases. Bacteria, fungi, virus and certain harmful insects are the main causative agents for such disease occurrence on the paddy crop. The diseases which affect the early stage of the paddy crops influences in the whole stage of crop cultivation. In early days of agriculture, the manual detection of diseases has been carried out by farmers. Image processing is one of the emerging techniques for identifying and classifying the different types of diseases and it overcomes the issues encountered during the manual detection of diseases. Image processing technique solves several issues involved in the cultivation of crops including, recognition and classification of plant diseases, discrimination of certain weeds and disease forecasting.

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.

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 Diabetic Maculopathy Detection using Image Processing

2019 ◽  
Vol 9 (1S3) ◽  
pp. 115-119
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Continuous Monitoring ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Human Beings ◽  
Diabetic Maculopathy ◽  
Monitoring Process ◽  
Physical Tests ◽  
Processing Techniques

Diabetic Retinopathy (DR) is a serious eye disease caused to human beings having diabetics. DR will affect the retina of the eye and even it may lead to complete blindness. It is essential to have an early treatment for the diagnosis of DR to avoid blindness. There are many physical tests like visual test, pupil dilation to detect retinopathy but all are time consuming processes. For diabetic retinopathy, it needs a continuous monitoring process. The main objective of this work is to detect diabetic maculopathy which is one of the major retinal abnormalities found among diabetic persons. Diabetic maculopathy is detected using image processing technique. In image processing techniques, we use image pre processing to reduce the noise and use segmentation process to extract the features of the macula. After that the features are compared using the classifier algorithm and the performances are measured using the accuracy, sensitivity and specificity.

scholarly journals Automatic Detection of Tomato Leaf Deficiency and its Result of Disease Occurrence through Image Processing

2019 ◽  
Vol 8 (11) ◽  
pp. 4165-4172
Keyword(s):  
Image Processing ◽  
Research Work ◽  
Nutrient Deficiency ◽  
Nutrient Content ◽  
Processing Technique ◽  
Tomato Leaf ◽  
Image Processing Technique ◽  
Tomato Production ◽  
Disease Occurrence ◽  
Segmented Images

Economic strength of a country is highly depends on agriculture productivity. More number of researches has been done based on detecting disease of plant by processing its leaf. However detecting disease after occurrence of it and identifying solution and source of it presence is a delay and useless process. The occurrence of disease in plants is due to absence of nutrient content in it. The main objective of this research work is to effectively detect deficiency in tomato leaf in order to protect it from disease occurrence. In our work, identification of deficiency in tomato leaf has been implemented using image processing technique. A plant grows in healthy way when it has its basic nutrients such as Nitrogen, phosphorus, potassium etc. Analyzing and detecting tomato plant leaf deficiency will identify occurrence of disease later. To detect deficiency in accurate way expectation and maximization segmentation process is implemented and features of segmented images have been extracted. Based on this extracted features classification is implemented to identify whether it is normal leaf or defected leaf. After identifying result the disease occurrence due to nutrient deficiency is shown. Therefore based on leaf image processing disease is identified by analyzing its deficiency in efficient way. Hence our research work prevents loss in tomato production and increases its growth and sales

scholarly journals Smart Leaf Recognition System

2020 ◽  
Vol 9 (5) ◽  
pp. 289-291
Keyword(s):  
Image Processing ◽  
Early Stage ◽  
Recognition System ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Raspberry Pi ◽  
Plant Leaves ◽  
Plant Leaf ◽  
Automatic Technique

One of major issue nowadays is the agricultural productivity which is something our Nation’s economy highly depends. Technology based advancements may lead to detection of diseases in plants which are quite natural. Care should be taken in this area before it causes serious effects on plants which mainly affect the product quality, quantity or productivity. Early stage detection of diseases in plants through some automatic technique is beneficial as it reduces a huge work of monitoring in large acres of crops. When they appear on plant leaves, earlier detection helps us to increase the yield and productivity. This paper presents an algorithm for image processing technique which is used for automatic detection and classification of plant leaf diseases with the help of raspberry pi and sensors. This survey is about different diseases and its classification, techniques which are used for plant leaf disease detection and also its respective fertilizer sprayed on the leaves.

Deep learning based intelligence cognitive vision drone for automatic plant diseases identification and spraying

2020 ◽  
Vol 39 (6) ◽  
pp. 8103-8114
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
R. Maheswar ◽  
V. Vijayakumar ◽  
Mohsin Butt
Keyword(s):  
Image Processing ◽  
Weather Conditions ◽  
Precision Farming ◽  
Processing Technique ◽  
Plant Diseases ◽  
Image Processing Technique ◽  
National Budget ◽  
Agriculture Industry ◽  
Average Accuracy ◽  
Integral Role

The agriculture industry is of great importance in many countries and plays a considerable role in the national budget. Also, there is an increased interest in plantation and its effect on the environment. With vast areas suitable for farming, countries are always encouraging farmers through various programs to increase national farming production. However, the vast areas and large farms make it difficult for farmers and workers to continually monitor these broad areas to protect the plants from diseases and various weather conditions. A new concept dubbed Precision Farming has recently surfaced in which the latest technologies play an integral role in the farming process. In this paper, we propose a SMART Drone system equipped with high precision cameras, high computing power with proposed image processing methodologies, and connectivity for precision farming. The SMART system will automatically monitor vast farming areas with precision, identify infected plants, decide on the chemical and exact amount to spray. Besides, the system is connected to the cloud server for sending the images so that the cloud system can generate reports, including prediction on crop yield. The system is equipped with a user-friendly Human Computer Interface (HCI) for communication with the farm base. This multidrone system can process vast areas of farmland daily. The Image processing technique proposed in this paper is a modified ResNet architecture. The system is compared with deep CNN architecture and other machine learning based systems. The ResNet architecture achieves the highest average accuracy of 99.78% on a dataset consisting of 70,295 leaf images for 26 different diseases of 14 plants. The results obtained were compared with the CNN results applied in this paper and other similar techniques in previous literature. The comparisons indicate that the proposed ResNet architecture performs better compared to other similar techniques.

A New Image Processing Technique for STEM

1974 ◽  
Vol 32 ◽  
pp. 432-433
Author(s):  
Yasushi Kokubo ◽  
Hirotami Koike ◽  
Teruo Someya
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Elastic Scattering ◽  
Field Emission ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Energy Analyzer ◽  
Scanning Microscope ◽  
Scanning Electron

One of the advantages of scanning electron microscopy is the capability for processing the image contrast, i.e., the image processing technique. Crewe et al were the first to apply this technique to a field emission scanning microscope and show images of individual atoms. They obtained a contrast which depended exclusively on the atomic numbers of specimen elements (Zcontrast), by displaying the images treated with the intensity ratio of elastically scattered to inelastically scattered electrons. The elastic scattering electrons were extracted by a solid detector and inelastic scattering electrons by an energy analyzer. We noted, however, that there is a possibility of the same contrast being obtained only by using an annular-type solid detector consisting of multiple concentric detector elements.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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