scholarly journals Development of Cotton Pest App for Decision Making among Cotton Farmers

2020 ◽  
pp. 164-171
Author(s):  
M. Kalpana ◽  
K. Senguttuvan ◽  
P. Latha
Keyword(s):  
Early Stage ◽  
Management Practice ◽  
Cloud Services ◽  
Cotton Field ◽  
Management Decision ◽  
Cotton Leaf ◽  
Leaf Disease ◽  
Cotton Farmers ◽  
Leaf Symptoms ◽  
Cotton Pest

Aims: In this paper Cotton Pest App is designed to identify the leaf disease in cotton at early stage. Cotton Pest App is an innovative application that is useful for farmers. Methodology: The farmers can capture the images in the cotton field and upload the images. Cotton API is created and placed in cloud services. Images taken from farmers field matches with Cotton API and gets the TNAU recommendation for the cotton leaf diseases. Results: Cotton Pest App for pest management in cotton will analyze and provide an accurate recommendation to farmers about the type of pesticide for the symptoms given through the images. Conclusion: This paper expresses the idea about the creation of Cotton Pest App, an android application that helps to make management decision for cotton leaf symptoms. The study would provide a better understanding of the management practice required for the cotton leaf disease.

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

scholarly journals Tomato Leaf Disease Detection Using Hybrid CNN-RNN Model

2021 ◽  
Author(s):  
Hepzibah Elizabeth David ◽  
K. Ramalakshmi ◽  
R. Venkatesan ◽  
G. Hemalatha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Stage ◽  
Computational Effort ◽  
Tomato Leaf ◽  
Disease Detection ◽  
Tomato Production ◽  
Classification Image ◽  
Leaf Disease ◽  
Learning Techniques

Tomato crops are infected with various diseases that impair tomato production. The recognition of the tomato leaf disease at an early stage protects the tomato crops from getting affected. In the present generation, the emerging deep learning techniques Convolutional Neural Network (CNNs), Recurrent Neural Network (RNNs), Long-Short Term Memory (LSTMs) has manifested significant progress in image classification, image identification, and Sequence Predictions. Thus by using these computer vision-based deep learning techniques, we developed a new method for automatic leaf disease detection. This proposed model is a robust technique for tomato leaf disease identification that gives accurate and better results than other traditional methods. Early tomato leaf disease detection is made possible by using the hybrid CNN-RNN architecture which utilizes less computational effort. In this paper, the required methods for implementing the disease recognition model with results are briefly explained. This paper also mentions the scope of developing more reliable and effective means of classifying and detecting all plant species.

scholarly journals Detection and identification of a novel 16SrXIII subgroup phytoplasma associated with strawberry red leaf disease in Argentina

2015 ◽  
Vol 65 (Pt_8) ◽  
pp. 2741-2747 ◽  
Author(s):  
Franco D. Fernández ◽  
Natalia G. Meneguzzi ◽  
Fabiana A. Guzmán ◽  
Daniel S. Kirschbaum ◽  
Vilma C. Conci ◽  
...  
Keyword(s):  
Rflp Analysis ◽  
Rrna Gene ◽  
The Novel ◽  
Universal Primer ◽  
Mature Leaves ◽  
Leaf Disease ◽  
Detection And Identification ◽  
Rflp Profile ◽  
Young Leaves ◽  
Leaf Symptoms

Strawberry red leaf phytoplasma was found in strawberry plants from production fields in Lules (Tucumán province) and Bella Vista (Corrientes province), Argentina. Characteristic strawberry red leaf symptoms were stunting, young leaves with yellowing at the edges, mature leaves which curled and were reddish at the abaxial face, flower and fruit deformation and death. The pathogen was detected with phytoplasma-universal primer pairs P1/P7 followed by R16F2n/R16R2 as nested primers in 13 diseased plants. Based on RFLP and sequence analysis of the amplified 16S rRNA gene, the phytoplasma was related to the 16SrXIII group (Mexican periwinkle virescence). In silico the RFLP profile of all the samples analysed revealed the presence of a unique pattern, showing that the novel phytoplasma is different from all the phytoplasmas currently composing the 16SrXIII group. The phylogenetic analysis was consistent with RFLP analysis as the strawberry red leaf phytoplasma was grouped within the 16SrXIII group, but formed a particular cluster. On this basis, the Strawberry red leaf phytoplasma associated with strawberry red leaf disease was assigned to a new subgroup, 16SrXIII-F.

Detection of Plant Leaf Diseases by Applying Image Processing Schemes

2019 ◽  
Vol 16 (9) ◽  
pp. 3728-3734
Author(s):  
Navneet Kaur ◽  
V. Devendran ◽  
Sahil Verma
Keyword(s):  
Image Processing ◽  
Yield Loss ◽  
Early Stage ◽  
Crop Protection ◽  
Recognition System ◽  
Assessment System ◽  
Plant Leaf ◽  
Detection Techniques ◽  
Leaf Disease ◽  
Key Factor

Timely diagnosis of the disease is the key factor in agricultural productivity. If timely detection of the disease is not taken into account, it may lead to crop yield loss. Hence, agriculturists and agronomists face troubles to detect diseases successfully at an early stage or later stage. To support these personnels to diagnose disease syndromes in infected plants, deep learning plays an important role. The machine based recognition system based on image processing not only saves time but also is more robust and efficient in comparison to manual assessment system. It helps the growers to take timely steps involved in the judicious treatment of the concerned leaf diseases for crop protection. Maximizing the production or minimizing the production loss is the primary goal of automatic plant leaf disease recognition system. Following review presents some leaf disease detection techniques.

Identification of Tomato Leaf Disease Detection using Pretrained Deep Convolutional Neural Network Models

2020 ◽  
Vol 21 (4) ◽  
pp. 625-635
Author(s):  
Anandhakrishnan T ◽  
Jaisakthi S.M Murugaiyan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
Computing Time ◽  
Network Models ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models ◽  
Leaf Disease ◽  
Proposed Model ◽  
Deep Cnn

In this paper, we proposed a plant leaf disease identification model based on a Pretrained deep convolutional neural network (Deep CNN). The Deep CNN model is trained using an open dataset with 10 different classes of tomato leaves We observed that overall architectures which can increase the best performance of the model. The proposed model was trained using different training epochs, batch sizes and dropouts. The Xception has attained maximum accuracy compare with all other approaches. After an extensive simulation, the proposed model achieves classification accuracy better. This accuracy of the proposed work is greater than the accuracy of all other Pretrained approaches. The proposed model is also tested with respect to its consistency and reliability. The set of data used for this work was collected from the plant village dataset, including sick and healthy images. Models for detection of plant disease should predict the disease quickly and accurately in the early stage itself so that a proper precautionary measures can be applied to avoid further spread of the diseases. So, to reduce the main issue about the leaf diseases, we can analyze distinct kinds of deep neural network architectures in this research. From the outcomes, Xception has a constantly improving more to enhance the accuracy by increasing the number of epochs, without any indications of overfitting and decreasein quality. And Xception also generated a fine 99.45% precision in less computing time.

scholarly journals Growth of Newly Established Tilia platyphyllos ‘Rubra’ Roadside Trees in Response to Weed Control and Pruning

2010 ◽  
Vol 36 (1) ◽  
pp. 35-40
Author(s):  
Palle Kristoffersen ◽  
Oliver Bühler ◽  
Søren Larsen ◽  
Thomas Randrup
Keyword(s):  
Weed Control ◽  
Untreated Control ◽  
Early Stage ◽  
Management Practice ◽  
Diameter Ratio ◽  
Diameter Growth ◽  
Branch Diameter ◽  
Diameter Increment ◽  
Tilia Platyphyllos ◽  
Stem Diameter Growth

This tree establishment study investigates the effect of weed control and pruning treatments on stem and branch diameter increment of newly planted broad-leaved lime (Tilia platyphyllos ‘Rubra’) roadside trees. Weed control significantly increased stem circumference four years after establishment by 3.6 cm (1.4 in) from 24.5 cm (9.7 in, untreated control) to 28.1 cm (11.1 in). In terms of Danish nursery sales prices, this corresponds to an increase of tree cash value of 1201 DKK (160.90 €, 235.40 US$) per tree. Calculating with 400 DKK (53.60 €, 78.40 US$) as cost for contract weeding per hour, this corresponds to 0.75 hours per tree per year for a period of four years. In addition to weed control treatments, trees were pruned at establishment, two years after establishment, or at both times. None of the pruning treatments affected stem diameter growth, but branch diameter and branch:stem diameter ratio were significantly reduced by all pruning treatments. Branch diameter ranged from 40.1 mm (1.6 in) on unpruned trees to 34.6 mm (1.4 in on trees pruned both times. Branch:stem diameter ratio ranged from 0.54 on unpruned trees to 0.49 on trees pruned both times. In consequence, weed control is recommended as a strong management practice. Mild pruning is also considered advisable, if structural crown problems can be avoided at an early stage, and if the tree has to be prepared for later pruning operations.

scholarly journals Real-Time Plant Health Assessment via implementing Cloud-Based Scalable Transfer Learning on AWS DeepLens

2020 ◽  
Author(s):  
Asim Khan ◽  
Umair Nawaz ◽  
Anwaar Ulhaq ◽  
Randall W. Robinson
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Fruits And Vegetables ◽  
Early Stage ◽  
Economic Loss ◽  
Disease Diagnosis ◽  
Data Set ◽  
Plant Leaf ◽  
Leaf Disease

In the Agriculture sector, control of plant leaf diseases is crucial as it influences the quality and production of plant species with an impact on the economy of any country. Therefore, automated identification and classification of plant leaf disease at an early stage is essential to reduce economic loss and to conserve the specific species. Previously, to detect and classify plant leaf disease, various Machine Learning models have been proposed; however, they lack usability due to hardware incompatibility, limited scalability and inefficiency in practical usage. Our proposed DeepLens Classification and Detection Model (DCDM) approach deal with such limitations by introducing automated detection and classification of the leaf diseases in fruits (apple, grapes, peach and strawberry) and vegetables (potato and tomato) via scalable transfer learning on A.W.S. SageMaker and importing it on A.W.S. DeepLens for real-time practical usability. Cloud integration provides scalability and ubiquitous access to our approach. Our experiments on extensive image data set of healthy and unhealthy leaves of fruits and vegetables showed an accuracy of 98.78% with a real-time diagnosis of plant leaves diseases. We used forty thousand images for the training of deep learning model and then evaluated it on ten thousand images. The process of testing an image for disease diagnosis and classification using A.W.S. DeepLens on average took 0.349s, providing disease information to the user in less than a second.

scholarly journals LEAF DISEASE DETECTION OF CUCURBITS USING CNN

2021 ◽  
Vol 40 ◽  
pp. 03043
Author(s):  
Isha Agrawal ◽  
Prada Hegde ◽  
Pooja Shetty ◽  
Priyanka Shingane
Keyword(s):  
Large Scale ◽  
Plant Disease ◽  
Learning Algorithm ◽  
Early Stage ◽  
Economic Value ◽  
Disease Detection ◽  
Knowledge Processing ◽  
Leaf Disease ◽  
Deep Learning Algorithm ◽  
Work Knowledge

Identification of plant disease is tough in agribusiness arena. If it is inaccurate, there occurs a tremendous damage in the production and economical price. Leaf Disease detection requires huge amount of work, knowledge, processing time in plant disease. The most used and edible vegetable all over the world is from cucurbitaceous family. The crops under this family have great economic value in the food industry and its production is done in large scale. This family consists of 965 species. If any of these plants catch disease then there would be a tremendous loss in the production of this field yields. Thus, treating them at early stage is best way to prevent such losses. Hence, Deep Learning Algorithm like CNN can be used to detect the diseases of the plants. The leaves of the plants would be used as primary material for identification of the disease, as they are much more visible on the leaves.

Sign in / Sign up

Export Citation Format

Share Document