Cotton Leaf Diseases Research Articles

Machine Learning Technique for Precision Agriculture Applications in 5G-Based Internet of Things

Monitoring systems based on artificial intelligence (AI) and wireless sensors are in high demand and give exact data extraction and analysis. The main objective of this paper is to detect the most appropriate plant development parameters. This paper has the concept of reducing the hazards in agriculture and promoting intelligent farming. Advancement in agriculture is not new, but the AI-based wireless sensor will push intelligent agriculture to a new standard. The research goal of this work is to improve the prediction state using image processing-based machine learning techniques. The main objective of the paper, as described above, is to detect and control cotton leaf diseases. This paper comprises several aspects, including leaf disease detection, remote monitoring system depending on the server, moisture and temperature sensing, and soil sensing. Insects and pathogens are typically responsible for plant diseases that reduce productivity if not timely. This paper presents a method to monitor the soil quality and prevent cotton leaf diseases. The proposed system suggested uses a regression technique of artificial intelligence to identify and classify leaf diseases. The information would be delivered to farmers through the Android app after infection identification. The Android app also allows soil parameter values like moisture, humidity, and temperature to be displayed along with the chemical level in a container. The relay may be on/off to regulate the motor and chemical sprinkler system as required by using the Android app. In the proposed system, the SVM algorithm delivers the best accuracy in detecting various diseases and demonstrates its efficiency in the detection and control by the improvement of cultivation for the farmers.

Cotton Leaf Disease Prediction Using Transfer Learning

Cotton is one of the financially significant agricultural items in India, but it is exposed to different constraints in the leaf area. Mostly, these constraints are identified as diseases that are hard to detect with bare eyes. This study focused to develop a model to boost the detection of cotton leaf disease and pests using the deep learning technique. Basically here we did comparative study of own defined convolution neural network architecture and popular state of art CNN architecture. This study centered to foster a model to classify diseased and fresh Cotton plant and leaf using Deep Learning techniques.. For this exploration, almost 2400 examples (600 pictures in each class) were gotten to for the purpose of preparing. This created model is carried out utilizing python form 3.7.3 and the model is prepared on the profound learning bundle called Keras, TensorFlow supported, and Jupyter which are utilized as the formative climate. This model accomplished 96.4% accuracy of recognizing diseased and fresh cotton plant and leaf using transfer learning techniques.

Cotton Leaf Disease Detection Using Instance Segmentation

Cotton is one of the most important cash and fiber crops in India. Agricultural machine learning plays a very important role in this agricultural industry. In this paper, the use of an object detection algorithm namely Mask RCNN along with transfer learning is experimented to find out if it is a fit algorithm to detect cotton leaf diseases in practical situations. The model training accuracy is found as 94 % whereas total loss value is continuously decreasing as number of optimize iterations are increasing.

An Efficient Identification of American Cotton Leaf Diseases through Training Set

An Efficient Identification of American Cotton Leaf Diseases through Training Set

Few-shot cotton leaf spots disease classification based on metric learning

BackgroundCotton diceases seriously affect the yield and quality of cotton. The type of pest or disease suffered by cotton can be determined by the disease spots on the cotton leaves. This paper presents a few-shot learning framework that can be used for cotton leaf disease spot classification task. This can be used in preventing and controlling cotton diseases timely. First, disease spots on cotton leaf’s disease images are segmented by different methods, compared by using support vector machine (SVM) method and threshold segmentation, and discussed the suitable one. Then, with segmented disease spot images as input, a disease spot dataset is established, and the cotton leaf disease spots were classified using a classical convolutional neural network classifier, the structure and framework of convolutional neural network had been designed. At last, the features of two different images are extracted by a parallel two-way convolutional neural network with weight sharing. Then, the network uses a loss function to learn the metric space, in which similar leaf samples are close to each other and different leaf samples are far away from each other. In summary, this work can be regarded as a significang reference and the benchmark comparison for the follow-up studies of few-shot learning tasks in the agricultural field.ResultsTo achieve the classification of cotton leaf spots by small sample learning, a metric-based learning method was developed to extract cotton leaf spot features and classify the sick leaves. The threshold segmentation and SVM were compared in the extracting of leaf spot. The results showed that both of these two method can extract the leaf spot in a good performance, SVM expented more time, but the leaf spot which extracted from SVM was much more suitable for classifying, thus SVM method can retain much more information of leaf spot, such as color, shape, textures, ect, which can help classficating the leaf spot. In the process of leaf spot classification, the two-way parallel convolutional neural network was established for building the leaf spot feature extractor, and feature classifier is constructed. After establishing the metric space, KNN was used as the spot classifier, and for the construction of convolutional neural networks, commonly used models were selected for comparison, and a spatial structure optimizer (SSO) is introduced for local optimization of the model, include Vgg, DesenNet, and ResNet. Experimentally, it is demonstrated that the classification accuracy of DenseNet is the highest, compared to the other two networks, and the classification accuracy of S-DenseNet is 7.7% higher then DenseNet on average for different number of steps.ConclusionsAs the step increasing, the accuracy of DesenNet, and ResNet are all improved, and after using SSO, each of these neural networks can achieved better performance. But The extent of increase varies, DesenNet with SSO had been improved the most obviously.

Detection and Classification of Cotton Leaf Diseases Using Faster R-CNN on Field Condition Images

Detection and Classification of Cotton Leaf Diseases Using Faster R-CNN on Field Condition Images

Leveraging Convolutional Neural Network and Transfer Learning for Cotton Plant and Leaf Disease Recognition

Leveraging Convolutional Neural Network and Transfer Learning for Cotton Plant and Leaf Disease Recognition

COTTON LEAF DISEASE DETECTION USING ARTIFICIAL NEURAL NETWORK

It is difficult for farmers to observe disease in the naked eye. Different types of plant disease reduce the plant life cycle. The presence of disease in plants can be observed by the symptoms present in the leaf, stem or infection occur in the fruit. Our proposed system gives an effective way for the detection of disease in the plant through the infection occurs in the leaf. The first step in disease detection is capturing cotton leaf disease through the device. In the second step, cotton leaf images are pre-processed through image segmentation. in the third step, RGB components i.e., red, green, and blue are removed from the image of the leaf, then it is converted to an HSV image. In the fourth step, white and black images of the cotton leaf are made; defective parts in the images are indicated by white colour. In the next step, an artificial neural network is trained to distinguish the disease and healthy leaf. By using our proposed system disease can be detected at the early stage with an accuracy of 85%. Disease or infection should be identified at the initial stage otherwise it leads to lossof crops.

A 3-Stage Method for Disease Detection of Cotton Plant Leaf using Deep Learning CNN Algorithm

Agriculture is one of the significant occupation in various countries including India. As major part of the Indian financial system is reliant on agriculture production, the intense consideration to the concern of food production is essential. The nomenclature and recognition of crop infection got much significance in technical as well as economic in the Agricultural Industry. While keeping track of diseases in plants with the support of experts can be very expensive in agriculture region. There is a necessity for a method or system which can automatically identify the diseases as it can bring revolution in monitoring enormous fields of crop and then plant leaflet can be taken ca The detection of cotton leaf disease is a very important factor to prevent serious outbreak.re imme4diately after recognition of disease. The aim of this paper is to provide guidelines for the development of application which recognizes cotton plant leaf diseases. For availing this user need to upload the image of the cotton leaf and then with the help of image processing one can get a digitized colour image of a diseased leaf which can be further processed by applying CNN algorithm to predict the actual root cause for the cotton leaf disease.

Deep Learning-Based Image Processing for Cotton Leaf Disease and Pest Diagnosis

Cotton is one of the economically significant agricultural products in Ethiopia, but it is exposed to different constraints in the leaf area. Mostly, these constraints are identified as diseases and pests that are hard to detect with bare eyes. This study focused to develop a model to boost the detection of cotton leaf disease and pests using the deep learning technique, CNN. To do so, the researchers have used common cotton leaf disease and pests such as bacterial blight, spider mite, and leaf miner. K-fold cross-validation strategy was worn to dataset splitting and boosted generalization of the CNN model. For this research, nearly 2400 specimens (600 images in each class) were accessed for training purposes. This developed model is implemented using python version 3.7.3 and the model is equipped on the deep learning package called Keras, TensorFlow backed, and Jupyter which are used as the developmental environment. This model achieved an accuracy of 96.4% for identifying classes of leaf disease and pests in cotton plants. This revealed the feasibility of its usage in real-time applications and the potential need for IT-based solutions to support traditional or manual disease and pest’s identification.

A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases

Cotton is the natural fiber produced, and the commercial crop grown in monoculture on 2.5% of total agricultural land. Cotton is a drought-resistant crop that provides a reliable income to the farmers that grow under the area with a threat from climatic change. These cotton crops are being affected by bacterial, fungal, viral, and other parasitic diseases that may vary due to the climatic conditions resulting in the crop’s low productivity. The most prone to diseases is the leaf that results in the damage of the plant and sometimes the whole crop. Most of the diseases occur only on leaf parts of the cotton plant. The primary purpose of disease detection has always been to identify the diseases affecting the plant in the early stages using traditional techniques for better production. To detect these cotton leaf diseases appropriately, the prior knowledge and utilization of several image processing methods and machine learning techniques are helpful.

Computationally light deep learning framework to recognize cotton leaf diseases

Cotton is an important commodity because of its use in various industries across the globe. It is grown in many countries and is imported/exported as a cash crop due to its large utility. However, cotton yield is adversely affected by the existence of pests, viruses and pathogenic bacteria, etc. For the last one decade or so, several image processing/deep learning-based automatic plant leaf disease recognition methods have been developed but, unfortunately, they rarely address the cotton leaf diseases. The proposed work presents a simple yet efficient deep learning-based framework to recognize cotton leaf diseases. The proposed model is capable of achieving the near ideal accuracy with early convergence to save computational cost of training. Further, due to the unavailability of publicly available datasets for this crop, a dataset is also collected comprising of three diseases namely curl virus, bacterial blight and fusarium wilt in addition to the healthy leaf Images. These images were collected from the Internet and fields of Southern Punjab region in Pakistan where the cotton crop is grown on thousands of acres every year and is exported to the Europe and the US either as a raw material or in the form of knitted industrial/domestic products. Experimental results have shown that almost all variants of our proposed deep learning framework have shown remarkably good recognition accuracy and precision. However, proposed EfficientNet-B0 model achieves 99.95% accuracy in only 152 seconds with best generalization and fast inference.

Cotton Leaf Diseases Recognition Using Deep Learning and Genetic Algorithm

Globally, Pakistan ranks 4 in cotton production, 6 as an importer of raw cotton, and 3 in cotton consumption. Nearly 10% of GDP and 55% of the country's foreign exchange earnings depend on cotton products. Approximately 1.5 million people in Pakistan are engaged in the cotton value chain. However, several diseases such as Mildew, Leaf Spot, and Soreshine affect cotton production. Manual diagnosis is not a good solution due to several factors such as high cost and unavailability of an expert. Therefore, it is essential to develop an automated technique that can accurately detect and recognize these diseases at their early stages. In this study, a new technique is proposed using deep learning architecture with serially fused features and the best feature selection. The proposed architecture consists of the following steps: (a) a self-collected dataset of cotton diseases is prepared and labeled by an expert; (b) data augmentation is performed on the collected dataset to increase the number of images for better training at the earlier step; (c) a pre-trained deep learning model named ResNet101 is employed and trained through a transfer learning approach; (d) features are computed from the third and fourth last layers and serially combined into one matrix; (e) a genetic algorithm is applied to the combined matrix to select the best points for further recognition. For final recognition, a Cubic SVM approach was utilized and validated on a prepared dataset. On the newly prepared dataset, the highest achieved accuracy was 98.8% using Cubic SVM, which shows the perfection of the proposed framework..

Development of Cotton Pest App for Decision Making among Cotton Farmers

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.

Cotton Leaf Image Segmentation using Modified Factorization-Based Active Contour Method

Cotton plant is one of the most widely cultivated crop across worldwide. The leaf is one of the important parts which help in the food production. There are different cotton leaf diseases like Alternaria spot, foliar, bacterial blight, etc. which affects the agricultural yield. In order to detect the diseases, leaf region extraction becomes a significant task and to achieve this we use image processing techniques. Henceforth in this paper, a novel method used to extract the leaf region from a complex background. The proposed method is used for leaf extraction from complex background. The algorithm used in this method is modified factorization based active contour (MFACM) which helps in getting better output images. The database images used for research are acquired from the field using a digital camera. The proposed work is compared with existing active contour algorithms like Gradient Vector Flow (GVF), Adaptive Diffusion Flow (ADF), and Vector Flow Convolution (VFC). From the experiment, it can be observed that the proposed method is better than the other active contour methods in terms of computation time and the number of iterations. In addition to that segmented result is analyzed using specificity, sensitivity, precision which showed that our proposed method is better than the other methods.

Cotton Leaf Disease Detection Using Texture and Gradient Features

The detection of cotton leaf disease is a very important factor to prevent serious outbreak. Most cotton diseases are caused by fungi, bacteria, and insects. A new method is proposed for careful detection of diseases and timely handling to prevent the crops from heavy losses. A disease due to bacteria, insects and fungus occurs in the cotton leaves in the range of about 80-95%. In the proposed work, first the group of infected leaves and normal leaves are collected and the image preprocessing is done using Adaptive histogram equalization for enhancing the contrast. In feature extraction phase, texture and gradient feature are extracted using Local Binary Pattern (LBP), Histogram of Oriented Gradient (HOG) and Differential of Gaussian (DOG). K- Nearest neighbor classifier is applied to classify the leaf image as a unaffected or an affected leaf. A cotton leaf database is internally created to evaluate the efficacy of our algorithm. The validate results show that the proposed method achieved higher classification accuracy in lower computational time.

Image Processing System for Detecting and Recovering from Cotton Leaf Disease using The Android Application

A plant is the heart of an agricultural field and it improves source of energy as well as financial income. We discuss several types of leaf diseases in the plants and their identification process, symptoms of disease and remedy measures are explained. There is a huge source of plants, but due to the effect of environmental condition or as weather, climate change, many non-native diseases occur on plants and cause social, ecological and financial losses. Many of the farmers are unable to recognize new disease. So the farmer needs an expert person who recognizes the disease and symptoms appearing on the plants, this will increase the cost of farming. To reduce farming, we introduce Android Application which diagnosis and identify the type of disease and symptoms of disease on plant leaves. The Android application performs basic operations like color transformation, thresholding and edge detection. Our system work on such plant leaves which are affected by many diseases that is a virus, fungi, excess of nutrients and insects, etc.We use image processing to detect and classify these diseases. Implement image processing operation on an android mobile device. It identifies the actual type of disease and its symptoms. Finally, using SVM-GA classifier to diagnosis the cotton leaf disease. Using Android Application type of disease, symptoms, remedy measures and recovery suggestions are given at a very less time and low cost.

A Review: To Detect and Identify Cotton leaf disease based on pattern recognition technique.

A Review: To Detect and Identify Cotton leaf disease based on pattern recognition technique.

A Review on Efficient Identification of American Cotton Leaf Diseases through Training Set

-The cotton leaf disease detection is the process of detecting disease by analyzing their visual properties. The visual properties extraction process from the images is known as the feature extraction. The feature extraction process can be done using the various feature descriptors like SIFT, SURF or other most suitable candidate. The feature descriptors are then passed to the classifier for the evaluation of the feature. The classifier is the algorithm, which is used to classify the feature on the basis of its similarity with the training dataset. The training dataset is the collection of features previously extracted from the known objects (the leaves with specific disease in our case). The leaves with disease are classified on the basis of their similarity with the training dataset of disease samples previously described by the feature descriptors. In this paper, our aim is to solve the cotton disease detection problem using the image processing techniques automatically from the input image. The disease classification will primarily based upon the visibility of the disease on the cotton leaves, which further can be used for the identification using the classifier. The proposed model implementation would be done using the MATLAB simulator and the proposed model results would be obtained in the form of the accuracy, precision, recall, elapsed time and many other similar parameters.

Widespread Occurrence of Cotton leaf curl virus on Radish in Pakistan

The current epidemic of cotton leaf curl disease (CLCuD) in Pakistan started in 1988 with the natural host range limited to a few plant species in the family Malvaceae. However, we have observed expansion in the host range of the virus, and several non-Malvaceous plants were found to be infected with the virus. Characteristic symptoms of CLCuD such as leaf curl and enations have been observed on radish plants, primarily in kitchen gardens. However, in 1999, levels of infection of 10 to 90% were observed both in commercial fields and kitchen gardens in the Punjab province of Pakistan. Both symptomatic and nonsymptomatic samples were collected from five different locations. Total DNA was isolated, dot-blotted on nylon membrane, and a full-length clone corresponding to DNA A of cotton leaf curl virus was labeled with 32P dCTP and used as a probe for the detection of a begomovirus. Strong signals were observed in symptomatic plants while no signals were observed in nonsymptomatic plants. Infection with a begomovirus was further confirmed by polymerase chain reaction (PCR) using degenerate primers for DNA A (1). Primers specific for the two distinct begomoviruses associated with CLCuD were also used in PCR reactions (2), and products of the expected size were obtained from all symptomatic samples, confirming infection with begomoviruses similar to those associated with CLCuD. A full-length probe of a nanovirus-like molecule associated with cotton leaf disease (3), called DNA 1 was labeled with 32P dCTP and detected the virus only in symptomatic plants. Similarly, primers specific for DNA 1 (3) amplified a product of expected size when used in PCR. On the basis of symptomatology and the detection of specific viral components associated with the disease, we confirmed that radish plants are infected with Cotton leaf curl virus (CLCuV). Since radish is a short duration crop, infection of CLCuV in radish may not serve as a direct source of infection for the next cotton crop. However, it is a potential threat to tomato crops which overlap with radish in the Punjab province. The detection of CLCuD in radish is another example of the mobilization of begomoviruses to previously unknown hosts.