scholarly journals Classifying Disease in Fruit using Machine Learning

2021 ◽  
pp. 309-313
Author(s):  
Ayeesha ◽  
Fathima Zeela ◽  
Vijetha
Keyword(s):  
Fruits And Vegetables ◽  
Detection System ◽  
Early Stage ◽  
Object Identification ◽  
Vegetable Crops ◽  
Interest Points ◽  
Scale Invariant ◽  
Technological Support ◽  
Processing Theory ◽  
Fruit Disease

India is agricultural country and Indian farmer select wide selection of fruit and vegetable crops. The cultivation of crops can be improved by the technological support. Fruits and vegetables losses are caused by disease. Diseases are seen on the leaves and fruits of plant, therefore disease detection plays a crucial role in cultivation of crops. Pathogens, fungi, microorganism, bacteria and viruses are sorts of fruit diseases also unhealthy environment is responsible for diseases. There are many techniques to spot diseases in fruits in its early stages. Hence, there's a requirement of automatic fruit unwellness detection system within the early stage of the unwellness. The aim is to detect the fruit disease, this method take input as image of fruit and determine it as infected or non- infected. The proposed method is based on the use of Scale-invariant Feature Transform (SIFT) Model with the desirable goal of accurate and fast classification of fruits. The SIFT features are local and based on the appearance of the object at particular interest points, and are invariant to image scale and rotation. They are also robust to changes in illumination, noise, and minor changes in viewpoint on image processing theory. SIFT have significant advantages because of their high accuracy, relatively easy to extract and allow for correct object identification with low probability of mismatch. Besides, they do not need an outsized number of coaching samples to avoid overfitting.

scholarly journals Improved the Agrobacterium tumefaciens-mediated transformation of cucumber by a modified the using of antibiotics and acetosyringone

2020 ◽  
Author(s):  
Li'ang Chai ◽  
Changxia Du ◽  
Huaifu Fan ◽  
Chen Liu ◽  
Yuyang Si
Keyword(s):  
Agrobacterium Tumefaciens ◽  
Culture Medium ◽  
Early Stage ◽  
Marker Gene ◽  
Vegetable Crops ◽  
Regenerated Plants ◽  
Npt Ii ◽  
Screening Efficiency ◽  
Speed Up ◽  
Polymerase Chain

Abstract Background: Cucumber (Cucumis sativus) is one of the most important vegetable crops in the world. As conventional breeding of cucumber is very challenging, genetic engineering is an alternative option to introduce important traits such as enhanced stress resistance and nutritional value. However, the efficiency of the transformation system depends on genotypes, transformation conditions, selection agents, etc. This study aims to speed up the process of Agrobacterium-mediated transformation of cucumber. ‘ Xintai mici ’, a very popular and typical north China-type cucumber variety, was transformed with Agrobacterium GV3101. The strain carried pCAMBIA2300s plasmid, a double vector with the marker gene of neomycin phosphotransferase II ( npt II). Results: The research results indicated that cefotaxime sodium was suitable for inhibiting Agrobacterium in the stage of screening and bud elongation. Timentin was best used during rooting stage. Furthermore, 25 mg/L kanamycin was used in the early stage of screening and increased to 50 mg/L for further screening. At the bud elongation and rooting stage, 75 and 100 mg/L kanamycin was used respectively to improve the screening efficiency. In order to obtain the highest regeneration frequency of resistant buds, 50, 150, and 100 μM acetosyringone were added in the pre-culture medium, infection solution, and co-culture medium respectively. To confirm the presence of the transgenes, DNA from npt II transgenic cucumber plants was analyzed by polymerase chain reaction after transplanting resistant regenerated plants. Conclusions: We finally achieved an 8.1% conversion, which was among the highest values reported until date using cucumber ‘ Xintai mici ’. Thus an effective protocol for Agrobacterium tumefaciens -mediated genetic transformation of cucumber was optimized.

scholarly journals Image Detection System Using Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 5356-5362
Author(s):  
Mohini Gawande
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Estimation Method ◽  
Scale Invariant ◽  
Matrix Estimation ◽  
Speeded Up Robust Features ◽  
Invariant Features ◽  
Marketing Analysis ◽  
New Strategies

The increasing popularity of Social Networks makes change the way people interact. These interactions produce a huge amount of data and it opens the door to new strategies and marketing analysis. According to Instagram and Tumblr, an average of 80 and 59 million photos respectively are published every day, and those pictures contain several implicit or explicit brand logos. Image recognition is one of the most important fields of image processing and computer vision. The CNNs are a very effective class of neural networks that is highly effective at the task of image classifying, object detection and other computer vision problems.in recent years, several scale- invariant features have been proposed in literature, this paper analyzes the usage of Speeded Up Robust Features (SURF) as local descriptors, and as we will see, they are not only scale-invariant features, but they also offer the advantage of being computed very efficiently. Furthermore, a fundamental matrix estimation method based on the RANSAC is applied.

scholarly journals Fruit Disease Detection Using Convolution Neural Network Approach

2018 ◽  
Vol 7 (2) ◽  
pp. 62-65
Author(s):  
Shivani . ◽  
Sharanjit Singh
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Texture Features ◽  
Convolution Neural Network ◽  
Image Feature ◽  
Disease Detection ◽  
Neural Network Approach ◽  
Disease Information ◽  
Fruit Disease ◽  
Farming Industry

Fruit disease detection is critical at early stage since it will affect the farming industry. Farming industry is critical for the growth of the economic conditions of India. To this end, proposed system uses universal filter for the enhancement of image captured from source. This filter eliminates the noise if any from the image. This filter is not only tackle’s salt and pepper noise but also Gaussian noise from the image. Feature extraction operation is applied to extract colour and texture features. Segmented image so obtained is applied with Convolution neural network and k mean clustering for classification. CNN layers are applied to obtain optimised result in terms of classification accuracy. Clustering operation increases the speed with which classification operation is performed. The clusters contain the information about the disease information. Since clusters are formed so entire feature set is not required to be searched. Labelling information is compared against the appropriate clusters only. Results are improved by significant margin proving worth of the study.

scholarly journals Determination of Selected Priority Pesticides in High Water Fruits and Vegetables by Modified QuEChERS and GC-ECD with GC-MS/MS Confirmation

Molecules ◽  
2019 ◽  
Vol 24 (3) ◽  
pp. 417 ◽  
Author(s):  
Maciej Tankiewicz
Keyword(s):  
Water Content ◽  
Fruits And Vegetables ◽  
High Water ◽  
International Agency ◽  
High Water Content ◽  
Vegetable Crops ◽  
Fruit And Vegetable ◽  
Quechers Method ◽  
Relative Standard

A modified quick, easy, cheap, efficient, rugged and safe (QuEChERS) method coupled to gas chromatography with electron capture detector (GC-ECD) was developed for simultaneous determination of selected electronegative pesticides in fruits and vegetables with high water content. The chosen compounds are commonly detected in fruit and vegetable crops, and some of their metabolites have even been found in human urine. In addition, some of them are known or suspected carcinogens according to the International Agency for Research of Cancer. Extraction and clean up parameters were optimized, thus the original QuEChERS method was modified to decrease solvent usage, in accordance with ‘green chemistry’ principles. The proposed methodology was validated in terms of selectivity, specificity, linearity, precision and accuracy. The obtained limits of detection (LODs) for all investigated pesticides ranged from 5.6 µg·kg−1 to 15 µg·kg−1 and limits of quantification (LOQs) from 17 µg·kg−1 to 45 µg·kg−1. The obtained data demonstrated the good reproducibility and stability of the procedure in the tested concentration range up to 10 mg·kg−1, with relative standard deviations (RSDs) lower than 10%. Recoveries for spiked pear samples at LOQ level for each pesticide were from 90% to 107% with RSDs lower than 9.6%. The suitability of the developed procedure was tested on various fruit and vegetable samples available on the market at different seasons. The proposed methodology is applicable for detection and monitoring of selected pesticides not only in fruits and vegetables with high water content, but also in samples containing large amounts of pigments and dyes.

scholarly journals Computer Vision Application for Early Stage Smoke Detection on Ships

2016 ◽  
Vol 52 (1) ◽  
pp. 63-80
Author(s):  
Miroslav Bistrović ◽  
Jasmin Čelić ◽  
Domagoj Komorčec
Keyword(s):  
Detection System ◽  
Early Stage ◽  
Fire Detection ◽  
Water Mist ◽  
Smoke Detector ◽  
Smoke Detection ◽  
Engine Room ◽  
Computer Image Processing ◽  
Flame Detector ◽  
Safety Features

Nowadays, ship’s engine room is fire protected by automatic fire fighting systems, usually controlled from a place located outside the engine room. In order to activate the water mist extinguishing system automatically, at least two different fire detectors have to be activated. One of these detectors is a flame detector that is not hampered by various air flows caused by ventilation or draft and is rapidly activated and the other is smoke detector which is hampered by these flows causing its activation to be delayed. As a consequence, the automatic water mist extinguishing system is also delayed, allowing for fire expansion and its transfer to surrounding rooms. In addition to reliability of the ship’s fire detection system as one of the crucial safety features for the ship, cargo, crew and passengers, using a systematic approach in this research the emphasis is placed on the application of new methods in smoke detection such as the computer image processing and analysis, in order to achieve this goal. This paper describes the research carried out on board ship using the existing marine CCTV systems in early stages of smoke detection inside ship’s engine room, which could be seen as a significant contribution to accelerated suppression of unwanted consequences.

Detection of Relative Motion

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 71-71
Author(s):  
M A Hogervorst ◽  
A M L Kappers ◽  
J J Koenderink ◽  
J Bongaerts
Keyword(s):  
Reference Frame ◽  
Relative Motion ◽  
Detection System ◽  
Temporal Frequency ◽  
Critical Time ◽  
Critical Distance ◽  
Viewing Distance ◽  
Scale Invariant ◽  
Motion Direction ◽  
Threshold Amplitude

We measured human sensitivity to the relative motion of blobs moving in the peripheral visual field. The stimuli consisted of one or two blobs (Gaussian luminance profiles) oscillating relative to an (invisible) reference frame which rotated with constant angular velocity about a fixation point. We determined thresholds for detecting the oscillation of different configurations of one or two blobs as a function of velocity, eccentricity (viewing distance), and temporal frequency. By determining thresholds as a function of frequency the temporal characteristics of the detection system could be revealed. Thresholds are higher for oscillations in the motion direction of the reference frame than perpendicular to it. No influence has been found of the position of the blobs in the frame of reference. The thresholds are scale-invariant. For low frequencies (<2 Hz) the threshold amplitude of the velocity modulation is constant whereas for high frequencies (>2 Hz) the threshold amplitude of the position modulation is constant. This behaviour can be well described by a model which detects the oscillations whenever, within a critical time (of about 200 ms for two blobs), the relative displacement is larger than a critical distance. The critical distance shows the same dependence on velocity as the span in the bilocal detector model of Koenderink et al (1985 Journal of the Optical Society of America A2 252 – 259).

scholarly journals Scale Invariant and Noise Robust Interest Points With Shearlets

2017 ◽  
Vol 26 (6) ◽  
pp. 2853-2867 ◽  
Author(s):  
Miguel A. Duval-Poo ◽  
Nicoletta Noceti ◽  
Francesca Odone ◽  
Ernesto De Vito
Keyword(s):  
Interest Points ◽  
Scale Invariant ◽  
Noise Robust
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