Sugarcane Leaf Disease Detection Using Deep Learning

<p>The detection of leaf diseases using modern technology has significant importance in agriculture and artificial intelligence. Deep learning, specifically, plays a crucial role in this field, as it enables accurate and efficient disease classification. Early detection of leaf diseases is vital to implementing timely treatments and preventing widespread damage to leaves. Leaf diseases can be caused by various factors, including bacteria, fungi, viruses, and other pathogens. Among them, bacteria and viruses are the most invasive and can lead to substantial yield losses if not identified and treated promptly. Bacterial and viral infections are common in agricultural settings, affecting leaves of all types and ages. Our research aims to propose a transfer learning-based model for predicting leaf diseases using a dataset of leaf images. The images will be classified into healthy or diseased leaves based on extracted features. The proposed model, named Leaf Disease Transfer Learning Algorithm (LDTLA), demonstrates promising results with an average accuracy of 97.37% on the dataset. Utilizing convolutional neural networks (CNN) and deep learning techniques, our LDTLA model outperforms previous quantitative and qualitative research studies in leaf disease detection. This advanced approach to leaf disease identification holds the potential to revolutionize agriculture by enabling farmers to make informed decisions, implement targeted treatments, and minimize leaf losses caused by diseases.<strong></strong></p>

Plant diseases are a severe threat to the environment, economy, and health. Early disease identification remains a challenging task in Iraq due to the scarce of the necessary resources and infrastructure. This paper uses various deep learning algorithms to detect different diseases on plant leaves and detect healthy ones, using an RGB camera as a crucial part of our real-time autonomous greenhouses' robot along with using two datasets, plant-village and cotton dataset, to investigate the best convolutional neural network architecture. The first dataset contained 10,190 images from the plant-village open datasets; it includes four crops with ten distinct classes of diseased and healthy leaves. Moreover, the cotton dataset contained 2,204 images for training and 106 images for testing; it has four classes of diseased and healthy plants and leaves. Different network architectures were tested in this paper for the best suitable lightweight architecture for our mobile robot. Results show that the best performance is 99.908% which achieved by the VGG16 network. The highest accuracy of VGG16 obtained in our research makes it the best tool for our autonomous plant disease detection robot. Index Terms— Convolutional neural networks architectures, Computer vision, Deep learning, Leaf disease detection and classification, Precision agriculture.

Abstract: This paper proposes an advanced convolutional neural network (ECNN) for the automatic detection and classification of leaf diseases towards precision agriculture. The proposed approach was tested on a standard plant leaf dataset with 54,306 images belonging to 38 classes of leaf diseases, with an 80:20 train/test split. Multi-scale convolutional layers and an attention refinement layer were incorporated to enhance the robustness of the approach in complex field environments. The experimental results show that the proposed ECNN approach resulted in an overall classification accuracy of 98.7%, with precision, recall, and F1-measure values of 98.4%, 98.6%, and 98.5%, respectively. Compared with the latest state-of-the-art models such as EnConv, EfficientNet-B0, and hybrid CNN models, the proposed approach improved the accuracy by 2.1% and reduced the computational complexity by 18.3%. The proposed approach was also found to be reliable in early-stage disease detection and under varying illumination conditions, making it suitable for real-time implementation in smart farming systems. Keywords: Leaf Disease Detection, Enhanced CNN, Precision Agriculture, Attention Mechanism and Deep Learning

The classification of plant leaf diseases is a critical step in safeguarding global food security and ensuring sustainable agricultural productivity.Recent advances in artificial intelligence (AI) and deep learning have accelerated the development of automated disease detection systems, improving both efficiency and diagnostic accuracy.This study introduces a novel hybrid strategy that integrates Convolutional Neural Networks (CNN) with traditional machine learning classifiers, specifically Support Vector Machines (SVM) and XGBoost, to leverage the complementary strengths of both paradigms.Two hybrid variants, CNN+SVM and CNN+XGBoost, are proposed, alongside the use of Particle Swarm Optimization (PSO) for feature selection, enabling the extraction of the most informative features for robust classification.The dataset used for experimentation comprises 15,000 maize leaf images, covering four major diseases: Northern Corn Leaf Blight, Common Rust, Brown Spot, and Gray Leaf Spot, along with healthy samples.Experimental evaluations confirm that the hybrid models outperform standalone CNN classifiers, achieving superior accuracy and generalization.Specifically, CNN+XGBoost achieved 95.2% accuracy, 93% precision, 96.3% recall, and 94.3% F1-score, while CNN+SVM achieved 92.7% accuracy, 91.1% precision, 94% recall, and 92.2% F1-score.These results demonstrate the synergistic benefits of combining CNNs with classical classifiers, while PSO significantly enhanced classification outcomes by identifying highly discriminative features.This research contributes to the advancement of precision agriculture by presenting a scalable hybrid framework for maize disease detection, offering timely diagnostic insights for farmers and promoting sustainable crop management practices.

Plant leaves and crops play a crucial role as a primary food source globally, making significant contributions to dietary iron intake (9%) and energy consumption (23%) per capita in the Asian region. Bacterial, yeast, and other microbial diseases pose significant challenges to farmers as they detrimentally impact plant health and reduce crop productivity. The manual diagnosis of these diseases poses a considerable challenge, particularly in regions with a scarcity of professionals specializing in leaves and crop protection. Automating leaf disease detection and providing easily accessible decision-support resources are crucial for facilitating efficient leaf protection strategies and mitigating crop damage. Despite multiple classification methods for diagnosing leaf diseases, a secure and accurate approach that fulfills these requirements has not yet been identified. This paper presents an architectural framework called Lightweight Federated Transfer Learning (LFTL) that addresses the challenge of Leaf Disease Detection and Classification (LDDC) while ensuring data privacy limitations are upheld. A dataset consisting of leaf disease images has been compiled, characterized by an imbalance in the distribution of the diseases. The collection includes four conditions: bacterial decay, brown spot, blast, and tungro, corresponding image counts of 1695, 1551, 1711, and 1419, respectively. Following the preprocessing stage, the LFTL framework was tested using both Independent and Identically Distributed (IID) and non-IID datasets. The study commenced with an efficacy evaluation of the Convolutional Neural Network (CNN) and eight TL models in the LDDC. The framework’s performance was evaluated across different circumstances and compared to conventional and federated learning models. The study’s findings revealed that the LFTL framework outperformed traditional distributed deep-learning classifiers, thus demonstrating its efficacy in individual and multiple client scenarios.

Leaf disease detection is a crucial task in modern agriculture, aiding in early diagnosis and prevention of crop infections. In this research paper, authors present a comprehensive study comparing nine widely used pre-trained models, namely DenseNet201, EfficientNetB3, EfficientNetB4, InceptionResNetV2, MobileNetV2, ResNet50, ResNet152, VGG16, and Xception, with our newly developed custom CNN (Convolutional Neural Network) for leaf disease detection. The objective is to determine if our custom CNN can match the performance of these established pre-trained models while maintaining superior efficiency. The authors trained and fine-tuned each pre-trained model and our custom CNN on a large dataset of labeled leaf images, covering various diseases and healthy states. Subsequently, the authors evaluated the models using standard metrics, including accuracy, precision, recall, and F1-score, to gauge their overall performance. Additionally, the authors analyzed computational efficiency regarding training time and memory consumption. Surprisingly, our results indicate that the custom CNN performs comparable to the pre-trained models, despite their sophisticated architectures and extensive pre-training on massive datasets. Moreover, our custom CNN demonstrates superior efficiency, outperforming the pre-trained models regarding training speed and memory requirements. These findings highlight the potential of custom CNN architectures for leaf disease detection tasks, offering a compelling alternative to the commonly used pre-trained models. The efficiency gains achieved by our custom CNN can be beneficial in resource-constrained environments, enabling faster inference and deployment of leaf disease detection systems. Overall, our research contributes to the advancement of agricultural technology by presenting a robust and efficient solution for the early detection of leaf diseases, thereby aiding in crop protection and yield enhancement.

The economic gainful of the country largely join on the crop production quality and quantity. The production profit can be improved by leaf disease detection in correct time. Many image processing methods have been developed for leaf disease detection. Technological improvement allows the task easier and faster at very early stage. Leaf disease is one of the major issue in the agriculture field. The Cotton leaves are affected by the disease named Cercospora, Bacterial blight, Ascochyta blight, and Target spot. General observation by farmers may time consume, expensive and sometimes inaccurate. For this, we introduce Deep Convolutional Neural Network based approach for identifying Cotton leaf diseases automatically. As per our understanding, Deep Convolutional Neural Network is used for the first time to detect cotton leaf disease. We train our data and assess the experimental result which displays the average accuracy of 96 %.

Agriculture impacts life and economic status of the people. It employs many people and accounts for major part of Gross Domestic Product. Diseases are quite natural and are a major factor for crop losses. Improper management of diseases results in annual loss of agricultural yield which will have serious effects on the quality, quantity and productivity if no proper care is taken. By using some automatic technique such as image processing, detection of leaf disease is quite significant and beneficial. The use of the most utilized deep learning classification mechanism, Convolutional Neural Network, helps in this regard.

A Convolutional Approach to Traffic Sign Detection presents a deep learning-based method using Convolutional Neural Networks (CNNs) for accurate traffic sign detection and classification. The system follows a two-stage pipeline: region proposal and sign classification, leveraging data augmentation and transfer learning for improved performance. It demonstrates robustness to challenges like poor lighting and occlusion and supports real-time operation, making it suitable for ADAS and autonomous vehicles. Experimental results show high accuracy across datasets, highlighting its potential in enhancing road safety and intelligent transportation systems. Index Terms — Traffic Sign Detection, Convolutional Neural Networks (CNN), Deep Learning, Computer Vision, GTSRB, Image Classification, Object Detection, Real-Time Detection, Autonomous Driving, Data Augmentation, OpenCV, Keras, TensorFlow, Image Preprocessing, Road Safety

Abstract: Agriculture is the most important sector among all, as it supplies food to people. As the food demand is increasing in our daily life and the population is increasing there is need of increasingin the food production also. The main issue nowadays is that diseases that are causing the food production to reduce. So our main aim is identification of the disease of the plants in early stage so that we can do the early diagnosis and protect crops from the harmful diseases. By doing this we can avoid the damage of crops and also can reduce the loses that farmers suffer from damage of crops. Here we are using image processing technique to identify the disease of the crops so that we can cure the diseases of the crops in early stage. Here we are using Deep learning algorithms like Convolutional neural networks (CNN) for our project that is leaf disease detection. CNN is the best algorithm for image processing as it greatly increases the performance of our model. Nowadays deep learning is the current trend as it solves the most complicated problems easily and can get the highest accuracy.

Tomato plants are particularly vulnerable to leaf diseases, which can take a serious toll on crop yield and quality if not caught early. Traditionally, farmers and agricultural experts rely on manual inspection to spot these diseases a method that can be both slow and prone to mistakes. To streamline this process, our project introduces an automated system that uses machine learning and image processing to detect tomato leaf diseases more accurately and efficiently. We worked with a dataset of tomato leaf images that includes both healthy leaves and those affected by diseases like Early Blight, Late Blight, Leaf Mold, and Target Spot. Using this data, we trained a Convolutional Neural Network (CNN) to classify the images with high accuracy. To improve the model’s learning, we applied standard preprocessing steps such as image augmentation, resizing, and normalization. Our experiments show that the system performs well, offering reliable early detection of various tomato leaf diseases. By providing timely insights, this tool can help farmers take preventive action sooner, potentially reducing crop loss and improving productivity. In the future, we hope to expand this work into mobile platforms to make real- time disease detection more accessible in the field. Key Words: Tomato leaf diseases, disease detection, deep learning, CNN, image processing, Late blight, Leaf mold.

India's agriculture is primary occupation and it experiences a yearly loss of 35% in crop productivity due to plant-related issues. Advances in technology, such as precision agriculture and the use of Deep learning algorithms, are helping to make this possible. A number of practical and technological challenges, including dataset collecting and labelling, variability in plant leaf images, inter-class similarity, imbalanced datasets, generalization, and real-time detection, are prevalent in the challenging task of detecting plant leaf disease. The main goal of utilizing a convolutional neural network (CNN) for plant leaf disease detection is to precisely categorize images of plant leaves into categories of healthy or diseased. Depending on the particular application scenario, there could be additional goals that researchers try to accomplish with their CNN models in addition to this main one. Tomato is a widely cultivated vegetable and is an important food crop worldwide. Employing machine learning and image processing methods for detecting diseases in tomato plant leaves can be highly efficient. These techniques can be used to analyze images of tomato leaves taken in the field, and an accurate algorithm can be used to identify the presence of disease symptoms. One such approach is using Convolutional Neural Networks (CNNs) algorithm which have been trained on a dataset of images of tomato leaves with and without disease symptoms. This can be done by gathering the images of the healthy tomato leaves and the infected ones and then training a CNN model to detect the difference. The trained model can categorize new images of tomato leaves into healthy or infected, offering a quick and precise method to identify diseases in tomato plants' leaves.

This composition bargains with the Tomato leaf infection discovery and classification utilizing different strategies like Convolutional Neural Network (CNN), Regions with CNN (R-CNN), Fast R-CNN and Faster R-CNN. The main issue in the agricultural sector is leaf diseases, which have an impact on crop yield and financial gain. Early detection of leaf diseases in plants is essential to prevent losses to the agricultural sector. There are several tomato leaf diseases that affect the crop's leaves, including the Mosaic virus, Early Blight, Healthy, Septoria leaf, and Bacterial spot. Using deep learning algorithms and image processing methods, we can identify the diseases in tomato leaves using developing deep learning approaches. The implementation procedures in our proposed work involves data collection, pre-processing, training, feature extraction, testing, and classification utilising the Visual Geometry Group (VGG 16) to identify damaged or healthy leaves. VGG 16 is incorporated to categorise the leaves as healthy or diseased based on the data and Regression's boundary box method is adopted. Therefore, using Faster RCNN, a model is created to identify and categorise diseases from every image of a tomato leaf that is used as an input, providing a forecast with a considerably greater degree of accuracy. We obtain an accuracy of approximately 98% after fitting the collected features into the neural network over 20 iterations.

The advent of deep learning has paved way for more efficient computer vision applications. In agricultural crop monitoring with technology driven approaches, it is indispensable to have plant disease identification. Recent research reveals that Convolutional Neural Network (CNN) is most suitable deep learning method to process leaf images for detecting diseases. As symptoms of leaf disease appear in specific area, considering entire leaf for processing incurs more computational cost and time besides deteriorating performance due to inadequate quality of training. To overcome this problem, we proposed a framework that considers extraction of ROI using deep CNN prior to prediction of pre-trained deep learning models such as VGG13, ResNet34, DenseNet19, AlexNet, Sqeezenet1_1 and Inception_v3. An algorithm named ROI Feature Map Creation (ROI-FMC) is defined to extract ROI for given input image. This will be given as input to another algorithm proposed namely ROI based Deep CNN with Transfer Learning for Leaf Disease Prediction (ROIDCNN-LDP). The latter is used to predict leaf diseases. PlantVillage dataset is used for empirical study. The experimental results revealed that with ROI awareness, all models could perform well. However, Inception_v3 is the deep CNN model that outperforms other models.

Deep Learning (DL) is a fastest growing and a broader part of machine learning family. Deep learning uses Convolutional Neural Networks (CNN) for image classification as it gives the most accurate results in solving real- world problem. CNN has various pre-trained architecture like AlexNet, GoogleNet, DenseNet, SqueezeNet, ResNet, VGGNet etc. In this study, we have used CNN and AlexNet architecture for detecting the disease in Mango and Potato leaf and compare the accuracy and efficiency between these architectures. The dataset containing 4004 images were used for this work. The images for potato were taken from plantvillage website, while images for mango were collected from GBPUAT field location. The results show that accuracy achieved from AlexNet is higher than CNN architecture.