Paper Published at IJAREEIE Journal 

Link: http://http://www.ijareeie.com/volume-11-issue-2

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Cotton Plant Disease Detection Using Machine Learning

   In this blog, we show how neural networks can be used for plant disease recognition in the context of image classification. We used the publicly available Plant Village dataset which has cotton-related diseases. We compared five different backbones including VGG16, ResNet50, InceptionV3, InceptionResNet and DenseNet169. We found that ResNet50 achieves the best result on the test set. For evaluation, we used metrics: accuracy, precision, recall, F1 score, and class-wise confusion metric. Our model achieves the best of results using ResNet50 with an accuracy of 0.982, the precision of 0.94, recall of 0.94, and F1 score of 0.94.

DATA SET:

A dataset contains 1951 images of disease and healthy cotton plants leave under controlled conditions. The images are further divided into 3 parts: 1)Test

                    2)Train

                    3)Validity

All three categories are further divided into four parts: 

1)Disease cotton plant 

2)Disease cotton leaves

3)Fresh cotton plant

4)Fresh cotton leaves

There are mainly 5 types of cotton disease:

1) Alternaria leaf spot alternaria macrospora.

2) Asochyta blight Asochyta gossypii.

3) Cercospora leaf spot Cercospora gossypina.

4) Fusarium wilt Fusarium oxysporum.

5) Target spot Corynespora cassiicola.

In the proposed work there are 1614 images in the training data set, 319 images in a validation data set, and 18 images in the testing data set. Out of 1614 training data images, 807 images belonging to the healthy category, and 807 images belonging to each cotton disease category described above.

 

MACHINE LEARNING:

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

In this, we use CNN(Convolution neural network) 

Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm that can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image, and be able to differentiate one from the other. It takes the steps of processing at every stage so that we could prevent major loss in both terms of goods and money.

CNN SUMMARY:

                

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Disease Detection Using Image Processing

 Hey Guys, 

We are designing the solution to detect the diseased part of cotton leaves and plants by finding the optimum way with minimum cost. Agricultural productivity is something on which the Economy highly depends. This is one of the reasons that disease detection in plants plays an important role in the agriculture field, as having the disease in plants is quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity, or productivity is affected.

PROBLEM STATEMENT:

As we all know that Indian economy depends on agriculture but leaf infection phenomena cause the loss of major crops results in economic loss. Leaf infection is the invasion of leaf tissues by disease-causing agents such as bacteria, virus, fungus, etc leading to degradation of the leaf as well as plant. This can be characterized by spots on the leaves, dryness of leaves, color change in leaves, and defoliation.  Automatic detection of plant diseases is an essential research topic as it may prove benefits in monitoring large fields of crops, and thus automatically detect the symptoms of diseases as soon as they appear on plant leaves. The proposed system is a software solution for the automatic detection and classification of plant leaf disease. 

PROPOSED SOLUTION:

Automatic detection of plant disease is an essential research topic as it

may prove a benefit in monitoring large fields of crop and thus

automatically detects the symptoms of a disease as they appear on the plant leaves. As the disease can be identified and the solution to the disease can be found. This information is sent to the farmer through GSM Modem. 

 It also covers a survey on different disease classification

techniques that can be used for plant leaves detection. Image segmentation, which is an important aspect of disease detection in plant leaf disease.

FEATURES:

➢ To detect the unhealthy regions of plant leaves.

➢ Classification of plant leaf disease using texture features.

➢ Coding is used to analyze leaf infection.

➢ The analyzed information/ result is sent via SMS to farmers.

➢ Plant disease identification by the visual way is a more laborious task and at

the same time, less accurate and can be done only in limited areas.

➢ Automatic detection of the diseases by just seeing the symptoms on the

plant leaves makes it easier as well as cheaper. This also supports

machine vision to provide image-based automatic process control.

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Plant Disease Detection Using Image Processing

 Introduction:

  The agricultural landmass is more than just being feeding sourcing in today’s world. Indian economy is highly dependent on agricultural productivity. Therefore in the field of agriculture, detection of disease in plants plays an important role. To detect a plant disease in the very initial stage, the use of an automatic disease detection technique is beneficial.

   The existing method for plant disease detection is simply naked eye observation by experts through which identification and detection of plant diseases are done. For doing so, a large team of experts as well as continuous monitoring of plant is required, which costs very high when we do with large farms. At the same time, in some countries, farmers do not have proper facilities or even ideas that they can contact experts. Due to which consulting experts even cost high as well as time-consuming too. In such conditions, the suggested technique proves to be beneficial in monitoring large fields of crops. Automatic detection of the diseases by just seeing the symptoms on the plant leaves makes it easier as well as cheaper.
                                                                             

    Plant disease identification by the visual way is a more laborious task and at the same time, less accurate and can be done only in limited areas. Whereas if automatic detection technique is used it will take fewer efforts, less time, and become more accurate. In plants, some general diseases seen are brown and yellow spots, early and late scorch, and others are fungal, viral, and bacterial diseases. Image processing is used for measuring an affected area of disease and to determine the difference in the color of the affected area.

Abstract:

  Agricultural productivity is something on which the economy highly depends. This is one of the reasons that disease detection in plants plays an important role in the agriculture field, as having the disease in plants is quite natural.  It also covers surveys on different disease classification techniques that can be used for plant leaf disease detection.   

   A color-based segmentation model is defined to segment the infected region placing it to its relevant classes.

  Disease detection involves steps like image acquisition, image preprocessing, image segmentation, features extraction, and classification. 

 i) Recognizing infected leaves and stem. 

ii) Measure the affected area. 

iii) Finding the shape of the infected region. 

iv) Determine the color of the infected region. 

v) And also influence the size and shape of the leaf

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