Automated Leaf Disease Detection System with Machine Learning

Abstract

This study provides a detailed review of the application of deep learning techniques in plant protection, with a particular emphasis on the detection of crop leaf diseases. Deep learning has received a lot of attention for its success in feature extraction and machine learning, and it has emerged as a major technique in a variety of disciplines such as image and video processing, audio processing, and natural language processing. When applied to the field of plant disease detection, deep learning allows for more objective and efficient extraction of disease traits, boosting research efficiency and technical improvements.Our study seeks to present a synthesis of recent advances in deep learning applied to agricultural leaf disease detection, highlighting current trends and addressing issues in the field. The paper is an invaluable resource for scholars working on plant pest identification. Specifically, our approach uses the Convolutional Neural Network (CNN) algorithm, attaining an outstanding accuracy rate of 97% in disease identification.

Introduction

I. INTRODUCTION

A subfield of machine learning known as "deep learning" is modeled after the human brain and is entirely dependent on artificial neural networks. due to the neural network's ability to imitate the human brain. Because there wasn't enough processing power and we had a lot of data earlier, it's all the rage these days. Neurons are the formal definition of deep learning. By learning to represent the world as a layered hierarchy of concepts, with each concept defined in relation to simpler ones and more abstract representations computed in terms of less abstract ones, deep learning is a specific type of machine learning that reaches considerable power and flexibility.

In addition to being essential for energy production, plants are also a major part of the solution to the global warming problem.  Nonetheless, a number of plant illnesses may result in devastating effects on the environment, society, and economy. By 2050, crop production must rise globally by at least 50% to fulfill the growing demand for food. The majority of agricultural production now takes place in Asia and Africa, where the majority of producers are family-run enterprises with no background in horticulture. As a result, illnesses and pests frequently result in yield losses of more than 50%. Identifying agricultural illnesses using the conventional human analytical method of visual inspection is no longer adequate.

II. LITERATURE SURVEY

The paper by Wasswa Shafik et al. provides a detailed analysis of plant disease detection, emphasizing the significance of accurate and efficient methods for identifying and managing plant diseases. It explores the utilization of advanced technologies such as AI, ML, and DL, particularly focusing on CNNs and attention mechanisms for disease recognition and localization. The study also addresses the challenges associated with disease detection, highlighting the need for standardized performance metrics and the bottleneck of disease localization. Additionally, the paper discusses the availability of datasets for training and testing models, along with emerging trends like the integration of diverse parameters for enhanced detection capabilities. The systematic review methodology employed in the study ensures a comprehensive evaluation of vision-based approaches in plant disease detection, offering valuable insights for future research and development in this critical field.[1].

The paper "Detection of Plant Leaf Diseases using CNN" by S. Bharath, K. Vishal Kumar, R. Pavithran, and T. Malathi, published in the International Research Journal of Engineering and Technology (IRJET) in May 2020, presents a system designed to facilitate the identification and classification of plant diseases using Convolutional Neural Networks (CNN). The system allows individuals without programming knowledge to predict leaf diseases by extracting shape and texture-oriented features from a dataset of 38 images representing various plant diseases. By leveraging CNN models, the system aims to enhance accuracy in disease detection, offering a user-friendly approach that could benefit farmers in decision-making processes related to crop cultivation and market demand.[2].

The authors of this paper are Appolinaire Tagne, Emmanuel Moupoujou, Anicet Tadonkemwa, and Florent Retraint The current paper introduces the FieldPlant dataset, a valuable resource comprising 5,170 annotated field leaf images collected from Cameroon plantations. Focused on diseases in tropical crops like corn, cassava, and tomato, this dataset is the first publicly available dataset with annotated cassava images for plant disease detection. By utilizing deep learning models, particularly convolutional neural networks (CNNs), researchers aim to empower farmers to identify plant diseases promptly and implement necessary countermeasures to prevent crop losses. The dataset's significance lies in its potential to train efficient models for plant disease detection using field images and object-detection techniques, thereby contributing to enhancing crop yield and food security on a global scale.[3].

The paper "Multi-Class Classification of Plant Leaf Diseases Using Feature Fusion of Deep Convolutional Neural Network and Local Binary Pattern" by Khalid M provides a comprehensive approach to classifying plant leaf diseases. In the realm of precision agriculture, the detection of plant diseases through image analysis stands as a critical area of research. Traditional methods relying on visual examination by experts have limitations in terms of cost and efficiency. To address this, Khalid M and team propose a novel approach in their paper on Multi-Class Classification of Plant Leaf Diseases. Their work introduces a deep convolutional neural network model that not only reduces training parameters and iteration times but also integrates deep features with Local Binary Pattern to capture intricate spatial texture details in plant leaf images. By achieving high accuracy rates across various plant leaf disease datasets, this research signifies a significant advancement in the field, offering a promising solution for improved disease control in agriculture.[4].

The paper titled "Plant Leaf Disease Detection Using Convolution Neural Network in Machine Learning" by Akshay Misal, Sagar Golhar, Triveni Awachat, Mohammad Muzaif, and Mrs.  provides a comprehensive review of the application of Convolutional Neural Networks (CNN) in plant leaf disease detection within the realm of machine learning. It discusses the utilization of deep learning models for accurately identifying and classifying various crop leaf diseases. The study emphasizes methodologies such as feature extraction, image preprocessing, and model training, highlighting the effectiveness of CNN models in achieving high levels of accuracy in disease detection tasks. The paper showcases the potential of deep learning to enhance efficiency in plant protection and agriculture.[5].

III. PROPOSED SOLUTION

One of the most significant jobs in the world is agriculture. It's significant since food is a basic requirement for all life on Earth. In the system that is being suggested, For recognition, the deep learning region-based convolutional neural network (R-CNN) technique is employed. They exist in two stages. the stages of testing and training. They acquire photos, preprocess the images, and use R-CNN to train the images in the beginning. The categorization and detection of leaf diseases constitute the second phase. For instruction photos are taken from the collection for analysis purposes, and real-time photos are accessible for testing. Identification of foliar disease is carried out using pictures that are added to the system or database. Image pre-processing is necessary for real-time environment input, and it then executes

This system detects the disease occurred to the plant leaf and provides the solution and step needed to be taken . our system aims to detect and  provide the accurate disease information occurred to the plant , it takes images detects the disease and provides the necessary steps to be taken ,  this system also provides the fertilizer to be used for that particular disease and it also provide the detail about which soil is best  for that particular plant .

The agricultural sector and associated stakeholders will gain from the suggested plant leaf disease detection system in a number of ways. Among these remedies are: Early Disease Detection: Farmers and other agriculture experts will be able to recognize diseases in their early stages thanks to the system's ability to detect plant leaf diseases early. Timely intervention and effective disease management depend on early detection. Enhanced Harvest Outcomes: The technology can help stop the spread of infections, lower crop losses, and eventually boost agricultural productivity by quickly diagnosing illnesses. This may help with issues related to global food security. Precise ailment Classification: The system will correctly categorize diseases affecting plant leaves and provide details on the nature and intensity of each individual ailment. Strategies for tailored therapy and care are made possible by this knowledge.
IV. METHODOLOGY

Plant leaf diseases are usually detected using a mix of machine learning algorithms, image processing techniques, and maybe other approaches. The following is a general process for identifying plant leaf diseases:

1. Gathering of Data: assemble a varied collection of plant leaf photos that includes both disease-free and healthy leaves. Make sure the dataset includes a range of plant types and disease progression phases. If diseases are present, annotate the photos using segmentation masks or bounding boxes.
2.  Preprocessing: To make processing easier, resize the pictures to a consistent size.If required, improve the image quality by altering the contrast and brightness, for example.
3. Feature Extraction: Identify the salient characteristics in the pictures that will aid in differentiating between healthy and sick leaves. These attributes could consist of texture and color histograms.
4. Training Models: Divide the dataset into sets for testing, validation, and training. Apply the labeled training data to your illness detection model training. If employing manually created features, traditional machine learning methods such as Support Vector Machines (SVM), Random Forests, etc. CNNs, or convolutional neural networks, are utilized in deep learning techniques. By adapting previously trained models—such as VGG, ResNet, etc. to fit your dataset, you can use transfer learning. To enhance the performance of the model, optimize the hyperparameters with the validation set.
5.  Model Evaluation: Utilizing the testing set, evaluate the trained model's performance. Evaluation criteria like accuracy, precision, recall, F1-score, etc. are frequently used. To learn how well the model performs in identifying various classifications of diseases and healthy leaves, examine the confusion matrix.
6. Post-Processing: We Used post-processing methods to improve the model's forecasts. This could be applying morphological operations, smoothing boundaries, or filtering out noise.
7. Constant Enhancement: As new methods or additional data become available, keep updating and improving the model. Track the model's performance in real-world scenarios and get input to pinpoint areas that need work.

V. IMPLEMENTATION

To identify and categorize diseases affecting plant leaves, a multi-step method combining image processing techniques and machine learning algorithms is used in plant leaf disease identification. Initially, cameras or other imaging devices are used to take digital pictures of plant leaves. The resolution, clarity, and lighting of these photos could differ. Preprocessing methods including scaling, normalization, and enhancement are used to improve the quality of the photos and standardize them for analysis. Following preprocessing, the photos go through feature extraction, which extracts pertinent data from the unprocessed pixel data. Characterizing the visual patterns linked to both healthy and sick leaves requires completing this stage. Shape properties, color histograms, texture descriptors, and spatial correlations are some examples of features that can be extracted from the photos. Convolutional neural networks (CNNs), in particular, are deep learning techniques that have gained prominence recently for automatically extracting discriminative features from picture data.

After feature extraction, labeled data is used to train machine learning models to identify patterns that correspond to various leaf disease kinds. When using CNN-based feature extraction, deep learning models are used, but conventional machine learning algorithms like support vector machines (SVM), random forests, and gradient boosting machines (GBM) can be used with handcrafted data. The models learn to discriminate between healthy and unhealthy leaves through iteratively modifying their internal parameters in order to reduce prediction errors during training.The models are used to categorize photos of unseen plant leaves after they have been taught. The models examine the features they have retrieved from the input photos and make predictions about the presence or absence of illnesses during inference. The model's predictions can be improved by using post-processing techniques like smoothing to increase segmentation accuracy or thresholding to eliminate false positives.

Utilizing the complementary abilities of machine learning and image processing, plant leaf disease detection offers a scalable and efficient way to track and manage plant health. Farmers and other agricultural experts may quickly identify ill plants, reduce crop losses, and decide on effective disease control measures by automating the detection process. This ultimately promotes sustainable agriculture and food security.

A. Algorithm:

1. Convolutional Neural Networks (CNNs): Convolutional Neural Networks (CNNs) serve an important role in computer vision, notably in projects such as plant leaf disease detection. CNNs are a sort of deep neural network that recognizes patterns in visual input, making them extremely useful for image classification applications. In the domain of plant leaf disease diagnosis, CNNs use convolutional layers to learn hierarchical features from input photos. These layers apply convolutional filters on the image, extracting key spatial and temporal features. Subsequent pooling layers contribute to reducing dimensionality and capturing the most relevant data.
2. Input Layer: In computer vision, the input layer often represents raw data, such as images.
3. Convolutional Layer: Convolutional layers perform convolutional operations on the input data. These processes require applying a series of filters (kernels) to the input to extract features like edges, textures, and patterns.
4. Activation Layer: Activation Layer: After each convolutional operation, an activation function, such as Rectified Linear Unit (ReLU), is applied element-wise to bring non-linearity into the model, allowing it to learn more complicated associations in data.
5. Pooling layer: Pooling layers reduce computing complexity by downsampling feature maps from convolutional layers, while keeping critical information. Max pooling and average pooling are popular approaches.
6. Fully Connected Layer: After convolution and pooling, data is flattened and fed into fully linked layers. These layers connect every neuron in one layer to every neuron in the following layer, allowing the model to acquire high-level representations.
7. Output Layer: The output layer generates the network's output. In classification problems, a softmax activation function is commonly used to generate probabilities for different classes.

VI. RESULT AND DISCUSSION

By applying a thorough technique, we were able to get encouraging results in our experiment on plant leaf disease detection. We carefully annotated the dataset to identify the presence of diseases after gathering a broad dataset made up of photos of plant leaves with various diseases and leaves that were healthy. To improve and standardize the image quality, preprocessing methods including contrast enhancement and scaling were used. We used the retrieved features to train various machine learning models, such as Support Vector Machines (SVM), Random Forests, and optimized Convolutional Neural Networks (CNNs), by utilizing both manually created features and deep learning-based feature extraction.

Our technology provides farmers with an economical and effective way to monitor crop health and apply targeted disease control measures by automating the disease identification process. Notwithstanding the encouraging outcomes, there are still a number of obstacles and restrictions. A significant constraint is the quality and accessibility of the dataset. Despite our best efforts, the dataset might not fully represent all diseases or environmental circumstances, even if it includes a variety of plant species and disease kinds. With a workable solution for early disease diagnosis and crop management, our plant leaf disease detection project makes a substantial contribution to the field of precision agriculture. Even if there are still obstacles and restrictions, further research efforts show promise for pushing the boundaries of current practices and achieving the full potential of AI-driven technology in sustainable agriculture.

Conclusion

The rising improvement of technology has led to a growing demand for automated monitoring and management systems. The primary cause of yield loss in agricultural fields is widespread disease. Identifying plant diseases is essential to maintaining crop yields. Because image analysis can quickly and effectively identify changes in plant health, it has shown to be a helpful tool in the detection of plant diseases. The project\'s outcomes will offer insightful information about the approach\'s potential and contribute to raising the general efficacy of plant disease detection.Agriculture is greatly impacted by plant leaf disease detection technologies because they make disease identification fast and precise. These systems enable farmers to make well-informed decisions, implement suitable farming techniques, and reduce production losses by promoting early intervention. Furthermore,By encouraging ecologically friendly disease management techniques and decreasing the need for chemical treatments, these technologies support sustainable agricultural practices. Plant leaf disease detection systems, in summary, are an essential first step toward sustainable agriculture. Farmers will be able to reduce the effects of disease, maximize crop yields, and contribute to global food security in the future if we take use of cutting-edge technologies and interdisciplinary cooperation.

References

[1] W. Shafik et al., \"A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends,\" in IEEE Access, vol. 11, pp. 59203, 2023. [2] S. Bharath, K. Vishal Kumar, R. Pavithran, and T. Malathi, \"Detection of Plant Leaf Diseases using CNN,\" International Research Journal of Engineering and Technology (IRJET), vol. 07, no. 05, pp. 5535-5539, May 2020. [3] A. Tagne, E. Moupoujou, A. Tadonkemwa, and F. Retraint, \"FieldPlant: A Dataset of Field Plant Images for Plant Disease Detection and Classification,\" in IEEE Transactions on Agricultural and Biological Systems, vol. 1, pp. 1-13, 2023. [4] K. M. Hosny et al., \"Multi-Class Classification of Plant Leaf Diseases Using Feature Fusion of Deep Convolutional Neural Network and Local Binary Pattern,\" in IEEE Access, vol. 11, pp. 62317, 2023.] [5] Misal, A., Golhar, S., Awachat, T., Muzaif, M., & Mrs.. \"Plant Leaf Disease Detection Using Convolution Neural Network in Machine Learning.\" In Proceedings of the International Research Journal of Engineering and Technology (IRJET), Volume 10, Issue 03, pp. 1086-1091,Mar 2023.

Copyright

Copyright © 2024 Ramesh Vishwakama, Ramashish Yadav, Harsheet Sharma, Dr. Saurabh Suman. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.