Chilli Plant Disease Detection and Classification using DenseNet CNN Approach

2021 ◽  
Vol 9 (VI) ◽  
pp. 4469-4477
Author(s):  
Sheenu N V
Keyword(s):  
Precision Agriculture ◽  
Plant Disease ◽  
Learning Algorithm ◽  
Plant Diseases ◽  
Disease Detection ◽  
Deep Learning Algorithm ◽  
Affected Plant ◽  
Main Motive ◽  
Important People ◽  
Micro Organisms

To fulfil the food requirement and economic growth, farming plays a very important role. Thus Farmers are the most important people in the world. Be it the smallest or the largest country, Because of them only we are able to live on the planet. Precision agriculture is the new trending term in the field of technology whose main motive is to reduce the workload of the farmers and increase the productivity of the farms by using technologies. So the aim of this work is to detect the disease of the plant by classifying their leaves using deep learning algorithm. For this work chilli plants are considered, because of their economic importance. And there are various problems in chilli production due to the presence of various micro-organisms and pathogens and The plant disease detection can be done by observing the spot on the leaves of the affected plant. The method here adopting to detect plant diseases is image processing using Dense Net based Convolution neural network (CNN). CNN will be used for leaf image classification and will produce the good results with a good accuracy.

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.

scholarly journals Leaf Disease Detection using Deep Learning Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 3172-3175
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Learning Algorithm ◽  
Image Feature ◽  
Disease Detection ◽  
Proper Solution ◽  
Classification Image ◽  
Deep Learning Algorithm ◽  
Crop Diseases ◽  
Crop Disease

India is a nation of agriculture and over 70 per cent of our population relies on farming. A portion of our national revenue comes from agriculture. Agriculturalists are facing loss due to various crop diseases and it becomes tedious for cultivators to monitor the crop regularly when the cultivated area is huge. So the plant disease detection is important in agriculture field. Timely and accurate disease detection is important for the loss caused due to crop diseases which affects adversely on crop quality and yield. Early diagnosis and intervention can reduce the loss of plant due to disease and reduce the unnecessary drug usage. Earlier, automatic detection of plant disease was performed by image processing. For disease detection and classification, image processing tools and the machine learning mechanism are proposed. Crop disease will be detected through various stages of image processing such as image acquisition, pre-processing of image, image feature extraction, feature classification, disease prediction and fertilizer recommendation.detection of disease is important because it will may help farmers to provide proper solution to prevent these disease.

scholarly journals A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

2020 ◽  
Vol 12 (19) ◽  
pp. 3188 ◽  
Author(s):  
Ning Zhang ◽  
Guijun Yang ◽  
Yuchun Pan ◽  
Xiaodong Yang ◽  
Liping Chen ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Plant Disease ◽  
Visual Assessment ◽  
Plant Diseases ◽  
Disease Detection ◽  
Biotic And Abiotic Stresses ◽  
Non Invasive ◽  
Host Pathogen Interaction ◽  
Typical Type ◽  
Disease Analysis

The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.

Fruit and Leaves Disease Prediction Using Deep Learning Algorithm

2019 ◽  
pp. 8-16
Author(s):  
Sathya V ◽  
Rafidha H ◽  
Sumitha Rani G
Keyword(s):  
Plant Disease ◽  
Learning Algorithm ◽  
Low Cost ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Production Plant ◽  
Deep Learning Algorithm ◽  
Long Time ◽  
The Cost ◽  
And Control

The purpose of Agriculture is not only to feed ever growing population but it’s an important source of energy and a solution to solve the problem of global warming. Plant diseases are extremely significant, as that can adversely affect both quality and quantity of crops in agriculture production. Plant disease diagnosis is very essential in earlier stage in order to cure and control them. Generally the naked eye method is used to identify the diseases. In this method experts are involved who have the ability to detect the changes in leaf color. This method involves lots of efforts, takes long time and also not practical for the large fields. Many times different experts identify the same disease as the different disease. This method is expensive as it requires continuous monitoring of experts. Tree leaves and fruit diseases can increase the cost of agricultural production and may extend to total economic disaster of a producer if not cured appropriately at early stages. The producers need to monitor their crops and detect the first symptoms in order to prevent the spread of a plant disease, with low cost and save the major part of the production. Hiring professional agriculturists may not be affordable especially in remote isolated geographic regions. Machine learning algorithm in image can offer an alternative solution in plant monitoring and such an approach may anyway be controlled by a professional to offer his services with lower cost. It includes image segmentation and image classification approach to predict various types of diseases using Otsu thresholding method and convolutional neural network method.

scholarly journals A Review on Advances in Automated Plant Disease Detection

2021 ◽  
Vol 11 (4) ◽  
pp. 251-264
Author(s):  
Radhika Bhagwat ◽  
Yogesh Dandawate
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Detection Methods ◽  
Economic Losses ◽  
Disease Detection ◽  
Range Images

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.

Plant Disease Detection Using Machine Learning Approaches

2021 ◽  
pp. 122-130
Author(s):  
Sukanta Ghosh ◽  
Shubhanshu Arya ◽  
Amar Singh
Keyword(s):  
Plant Disease ◽  
Crop Yields ◽  
Detection System ◽  
Visual Observation ◽  
Plant Diseases ◽  
Disease Detection ◽  
Learning Approaches ◽  
Factors Affecting ◽  
Plant Leaf ◽  
Market Situation

Agricultural production is one of the main factors affecting a country's domestic market situation. Many problems are the reasons for estimating crop yields, which vary in different parts of the world. Overuse of chemical fertilizers, uneven distribution of rainfall, and uneven soil fertility lead to plant diseases. This forces us to focus on effective methods for detecting plant diseases. It is important to find an effective plant disease detection technique. Plants need to be monitored from the beginning of their life cycle to avoid such diseases. Observation is a kind of visual observation, which is time-consuming, costly, and requires a lot of experience. For speeding up this process, it is necessary to automate the disease detection system. A lot of researchers have developed plant leaf detection systems based on various technologies. In this chapter, the authors discuss the potential of methods for detecting plant leaf diseases. It includes various steps such as image acquisition, image segmentation, feature extraction, and classification.

scholarly journals Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 939 ◽  
Author(s):  
Marko Arsenovic ◽  
Mirjana Karanovic ◽  
Srdjan Sladojevic ◽  
Andras Anderla ◽  
Darko Stefanovic
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Real Life ◽  
Weather Conditions ◽  
Plant Diseases ◽  
Disease Classification ◽  
Generative Adversarial Networks ◽  
Disease Detection ◽  
Single Leaf ◽  
The Impact

Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant disease detection models are presented and discussed in this paper. Furthermore, a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images. Images were taken in various weather conditions, at different angles, and daylight hours with an inconsistent background mimicking practical situations. Two approaches were used to augment the number of images in the dataset: traditional augmentation methods and state-of-the-art style generative adversarial networks. Several experiments were conducted to test the impact of training in a controlled environment and usage in real-life situations to accurately identify plant diseases in a complex background and in various conditions including the detection of multiple diseases in a single leaf. Finally, a novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment. The trained model achieved an accuracy of 93.67%.

scholarly journals Plant Disease Detection and Localization using GRADCAM

2020 ◽  
Vol 8 (6) ◽  
pp. 3069-3075
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Plant Diseases ◽  
Financial Resources ◽  
Disease Detection ◽  
The World ◽  
Detection And Localization ◽  
Better Than ◽  
Made In

Plant diseases are diseases that change or disrupt its important functions. The reduction in the age at which a plant dies is the main danger of plant diseases. And farmers around the world have to face the challenge of identifying and classifying these diseases and changing their treatments for each disease. This task becomes more difficult when they have to rely on naked eyes to identify diseases due to the lack of proper financial resources. But with the widespread use of smartphones by farmers and advances made in the field of deep learning, researchers around the world are trying to find a solution to this problem. Similarly, the purpose of this paper is to classify these diseases using deep learning and localize them on their respective leaves. We have considered two main models for classification called resnet and efficientnet and for localizing these diseases we have used GRADCAM and CAM. GRADCAM was able to localize diseases better than CAM

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