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.

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.

Detection of plant leaf disease using digital image processing

2020 ◽  
Author(s):  
Ramesh Kumar Mojjada ◽  
K. Kiran Kumar ◽  
Arvind Yadav ◽  
B.V.V. Satya Vara Prasad
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Plant Leaf ◽  
Leaf Disease

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 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.

Effective Data Augmentation and Training Techniques for Improving Deep Learning in Plant Leaf Disease Recognition

2021 ◽  
Author(s):  
Prem Enkvetchakul ◽  
Olarik Surinta
Keyword(s):  
Plant Disease ◽  
Data Augmentation ◽  
Recognition System ◽  
Deep Convolutional Neural Networks ◽  
Plant Leaf ◽  
Training Techniques ◽  
Leaf Disease ◽  
Training Technique ◽  
Augmentation Techniques ◽  
And Training

Plant disease is the most common problem in agriculture. Usually, the symptoms appear on leaves of the plants which allow farmers to diagnose and prevent the disease from spreading to other areas. An accurate and consistent plant disease recognition system can help prevent the spread of diseases and save maintenance costs. In this research, we present a plant leaf disease recognition system using two deep convolutional neural networks (CNNs); MobileNetV2 and NasNetMobile. These CNN architectures are designed to be suitable for smartphones due to the models being small. We have experimented on training techniques; online, offline, and mixed training techniques on two plant leaf diseases. As for data augmentation techniques, we found that the combination of rotation, shift, and zoom techniques significantly increases the performance of the CNN architectures. The experimental results show that the most accurate algorithm for plant leaf disease recognition is NASNetMobile architecture using transfer learning. Additionally, the most accurate result is obtained when combining the offline training technique with data augmentation techniques.

scholarly journals DETECTION OF PLANT LEAF DISEASES IN AGRICULTURE USING RECENT IMAGE PROCESSING TECHNIQUES – A REVIEW

2019 ◽  
Vol 7 (1) ◽  
pp. 29-33
Author(s):  
Arpan Singh Rajput ◽  
Shailja Shukla ◽  
S. S. Thakur
Keyword(s):  
Image Processing ◽  
State Of The Art ◽  
Disease Classification ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Processing Techniques ◽  
Large Work ◽  
Respective Product ◽  
Automatic Technique

Purpose: Agricultural productivity is something on which the economy highly depends in India as well in all over the world. India is an agriculture-dependent country; wherein about 70% of the population depends on agriculture. Methodology: This is one of the main reasons that disease detection in agriculture plays an important role, as having the disease in plant leaf is quite natural. If proper observations are not taken in the agriculture field then it causes serious effects on plants due to which respective product quality and productivity are affected. Detection of plant leaf disease through effective and accurate automatic technique is beneficial at the starting stage as it reduces a large work of monitoring in big farms of crops. Result: This paper presents the review on the state of the art disease classification techniques presently used using image processing that can be used for plant leaf disease detection in agriculture.

Plant Leaf Disease Detection Using Image Processing

2021 ◽  
pp. 161-168
Author(s):  
M. Sahana ◽  
H. Reshma ◽  
R. Pavithra ◽  
B. S. Kavya
Keyword(s):  
Image Processing ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease
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