Deep Learning-Based Leaf Disease Detection in Crop Using Images for Agricultural Application

Abstract: Plant diseases are a major threat to crop yield and food security. Early detection and diagnosis are crucial to prevent the spread of disease and minimize crop losses. Plant diseases can have devastating effects on crop yields and food security. Disease detection is crucial for effective disease management. In recent years, deep learning techniques, such as Convolutional Neural Networks (CNN), have shown promising results in disease detection. In this study, we propose a plant disease detection system using CNN and Arduino. The system involves capturing images of plant leaves using a camera module connected to an Arduino board. In recent years, deep learning techniques such as Convolutional Neural Networks (CNNs) have shown great promise in image-based disease detection. Additionally, the use of low-cost microcontrollers like Arduino can provide a costeffective solution for real-time disease detection in the field. This paper proposes a plant disease detection system that combines CNN and Arduino for early and accurate disease detection. The system utilizes a CNN model to classify plant disease images and an Arduino board to analyze the results and provide real-time feedback to the user. The proposed system is tested on a publicly available dataset of plant disease images, achieving high accuracy, and demonstrating its potential for practical use. The results show that the proposed system can provide an affordable and efficient solution for early plant disease detection, facilitating prompt action and reducing crop losses.

Agriculture is the most critical part of the Indian economy so to prevent production loss, disease attacks must be predicted and treated early. In plants with differing climatic conditions, disease is very normal and natural which decreases the crop productivity. Study into the use of image processing techniques for plant disease identification has become a hot subject in order to address these issues. Deep learning (DL) is the golden age of machine learning (ML), and it is now helping to identify and classify plant diseases early. This review investigates and analyzed recent methods on Deep learning, Transfer learning and convolution neural network for crop disease detection. First, a look at the deep learning architectures, data sources and various image processing techniques that were used to process the imaging data. Many DL architectures have recently been adopted, along with visualization tools, which are critical for identifying signs and classifying plant diseases. We also go through some of the unsolved issues that must be tackled in order to create functional automated plant disease recognition systems that can be used in the field.

Plant diseases are one of the major causes of crop yield reduction worldwide. Early detection and effective management of plant diseases will ensure a sustainable food supply and reduce famine. In this project, we propose a Plant Disease Management System (PDMS) that uses Artificial Intelligence (AI) and Internet of Things (IoT) technologies to diagnose and manage plant diseases. The proposed system consists of two main components: a plant disease diagnosis module and a disease management module. The plant disease diagnosis module uses AI algorithms to analyze images of plant leaves and identify the type of disease affecting the plant. The AI model is trained on a large dataset of plant images using deep learning techniques, such as Convolution Neural Networks (CNNs) and Transfer Learning.

The care and health of agricultural plants, which are the primary source for people to eat healthily, are essential. Disease detection in plants is one of the critical elements of smart agriculture. In parallel with the development of artificial intelligence, advancements in smart agriculture are also progressing. The development of deep learning techniques positively affects smart farming practices. Today, using deep learning and computer vision techniques, various plant diseases can be detected from images such as photographs. In this research, deep learning techniques were used to detect and diagnose bean leaf diseases. Healthy and diseased bean leaf images were used to train the convolutional neural network (CNN) model, which is one of the deep learning techniques. Transfer learning was applied to CNN models to detect plant diseases with the difference of related works. A transfer learning-based strategy to identify various diseases in plant varieties is demonstrated using leaf images of healthy and diseased plants from the Bean dataset. With the proposed method, 1295 images were studied. The results show that our technique successfully identified disease status in bean leaf images, achieving an accuracy of 98.33% with the ResNet50 model.

<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>

Agriculture serves as a fundamental pillar of Tunisia’s economy, providing employment and livelihood for a large segment of the population, thereby making it an essential sector for the country. Nevertheless, the sector faces challenges such as reduced agricultural output and quality due to factors like pesticide usage, water scarcity, climate change, and notably, plant diseases. These plant diseases represent one of the most significant threats to global agricultural productivity and quality. Traditional methods of plant disease detection heavily rely on expert identification, which can quickly cause delays in field management and crop disease control. The best strategy to manage these diseases and reduce crop loss is to put protective measures in place as soon as possible. To this purpose, machine learning and deep learning models-based method for the detection and classification of olive leaf disease is developed in order to improve the speed and precision of disease classification. In this study, we explore how machine learning and deep learning techniques may be used to improve agricultural practices and solve these issues. We specifically review the state-of-the-art literature on the use of deep learning and machine learning algorithms in diverse agricultural applications. We also presented a novel method for categorizing olive leaf diseases, by integrating deep learning architectures, VGG16, and fine tuning. Also, in this approach, we simulated a total of two distinct deep models utilizing pre-trained convolutional neural network architectures (VGG19 and VGG16) and along with four machine learning classifiers (RF, SVM, KNN and DT). In addition, we simulated a novel method for categorizing olive leaf diseases, by integrating deep learning architectures, VGG16, and fine tuning, our results findings showed that VGG16 with fine-tuning classifier is the best method for classification of healthy and diseased plant leaves. It achieves a testing accuracy score of 98% to beat the rest of the architectures.

Effective plant disease detection is paramount for ensuring global food security and sustainable agricultural practices. This article presents a comprehensive exploration of utilizing machine learning, specifically Convolutional Neural Networks (CNNs), for accurate and timely plant disease classification. The research delves into the integration of diverse datasets, preprocessing techniques, and CNN architecture to create a robust disease detection model. The dataset, encompassing various crops and diseases, forms the foundation for training the model, while preprocessing ensures optimal data quality. The CNN architecture, meticulously designed, progresses through convolutional and pooling layers to extract hierarchical features from input images. The trained model achieves an accuracy of 92.23% on disease classification, showcasing the potential of such technology in agricultural innovation. The article underscores the pivotal role of machine learning in revolutionizing plant disease detection, thus contributing to global food security and sustainable agriculture.

In recent years, plant leaf diseases has become a widespread problem for which an accurate research and rapid application of deep learning in plant disease classification is required, tomato is also one of the most important plants and seeds which are used worldwide for cooking in either dried or fresh form, tomato are a great source of protein that offer many health benefits, but there are a lot of diseases associated with tomato leaf which hinder its production. Thus, an accurate classification of tomato leaf diseases is needed to solve the problem in the early stage. A deep learning approach is proposed to identify and classify leaf disease by using public dataset of leaf image and CNN model with the open source library TensorFlow. In this project, we proposed a method to classify tomato leaf disease and to find and describe the efficient network architecture (hyper parameters and optimization methods). Moreover, after applying each architecture separately, we compared their obtained results to find out the best architecture configuration for classifying tomato leaf diseases and their results. Furthermore, to satisfy the classification requirements, the model was trained using CNN architecture check if we could get faster training times, higher accuracy and easier retraining. Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this project, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this project will be a valuable resource for researchers who study the detection of plant diseases. At the same time, we also discussed some of the current challenges and problems that need to be resolved.

A country’s financial growth is prejudiced by its rate of agricultural output. Nevertheless, Plant Diseases (PD) pose a substantial obstacle to the cultivation and value of foodstuff. The timely detection of PDs is paramount for public wellness and Sustainable Agriculture (SA) promotion. The conventional diagnostic procedure entails a pathologist’s visual evaluation of a particular plant through in-person visits. Nevertheless, the manual inspection of crop diseases is limited due to its low level of accuracy and the limited availability of skilled workers. To address these concerns, there is a need to develop automated methodologies capable of effectively identifying and classifying a wide range of PDs. The precise detection and categorization of PDs pose a challenging task due to various factors. These include the presence of low-intensity data in both the image’s backdrop and the forefront, the significant similarity in color between normal and diseased plant regions, the presence of noise in the specimens, and the variations in the location, chrominance, framework, and dimensions of plant leaves. This paper presents a novel approach for identifying and categorizing PDs using a Deep Convolutional Neural Network - Transfer Learning (DCNN-TL) technique in the Agricultural Operation System (AOS). The proposed method aims to enhance the capabilities of SA in accurately identifying and categorizing PDs. The improved Deep Learning (DL) methodology incorporates a TL technique based on fine-tuned Visual Geometry Group 19 (VGG19) architecture. The revised system accurately detects and diagnoses five distinct PD categories. Among the evaluated methods, the proposed DCNN-TL in this study shows outstanding precision, recall, and accuracy values of 0.996, 0.9994, and 0.9998, respectively.

Early diagnosis of pests and plant diseases is crucial for preventing significant crop losses. This study proposes a leaf disease detection system using MobileNetV2 integrated with multiple optimization techniques (Adam and learning rate scheduling). Evaluated on the Pepper PlantVillage dataset, the MobileNetV2 model employs patch embedding and attention mechanisms for feature extraction, with SoftMax used for final classification. The model was further validated on a multi-class Apple PlantVillage dataset. Results demonstrate high accuracy: 97.03% for pepper and 94.63% for apple classification. Comparative analysis with CNN architectures shows superior efficiency and faster convergence for our model, outperforming Inception v3 (96.81%) and VGG-19 (94.93%) on the pepper dataset.

In recent times, drastic changes in the climate have brought about a dramatic increase in the prevalence of crop diseases. Such diseases are a huge threat to sustainable agriculture, especially for third-world countries whose economies still rely much on agriculture. Proper disease detection is an essential prerequisite to get timely treatment of crops and prevent reduction in crop yield. In most plant diseases, the leaves may contain some texture variations, which may help in identifying the diseases. However, an identical disease may not appear in a similar fashion for different plant species and visible texture differences may be very negligible. In most cases, plant diseases are classified for all species together, which may miss the species-specific symptoms. In this paper, we propose a two-stage classification scheme to classify plant diseases that divides the whole classification pipeline into two parts: a plant species detection part and a subsequent species-specific disease detection part. In both stages, we employ some efficient deep convolutional neural network (CNN) architectures, namely EfficeintNetB3, NASNetLarge, DenseNet201, MobileNetV2, VGG16 and InceptionV3 with effective transfer learning. Two publicly available datasets, namely Plant Village and IPM, are considered in this paper. Comparative analysis reveals that the proposed two-stage method can perform better than the conventional single-stage methods reported in the literature for plant disease detection.

The advancement of artificial intelligence (AI) and the implementation of Deep Learning have made object identification more efficient through machine-assisted methods. Among the Deep Learning techniques, Convolutional Neural Networks (CNNs) show significant potential for application in agriculture, particularly in monitoring and managing plant diseases and pests. In 2022, global demand for vanilla increased sharply; however, Indonesia's vanilla exports accounted for only 2.63% of the world's total. One of the primary reasons for this low contribution is the prevalence of diseases and pests that inhibit plant growth. This research aims to examine the use of CNNs in identifying diseases in vanilla plants, specifically Sclerotium, Fusarium, and Colletotrichum. The approach involves training a CNN model to distinguish between images of healthy plants and those infected by diseases. The results demonstrate that the CNN model achieved an overall accuracy of 80% in correctly identifying both healthy and diseased plants. These findings exhibit that CNN technology is an effective service tool for early disease detection and management in vanilla cultivation, with the potential to enhance vanilla production in Indonesia.

Due to the vital importance of promptly controlling plant diseases and the challenges facing today's agricultural disease management techniques, a novel framework has been introduced for detecting crop disease. This paper proposes a new methodology for detecting cotton leaf disease using multi-modal deep learning approaches. Since the early control of plant diseases is paramount and commonly utilized methods for managing agricultural diseases suffer in practice, a new framework for disease detection has been proposed. Inspecting modern deep learning algorithms concerning the analysis of images and processing for pattern recognition, we design a system utilizing visual indicators of diseases from multiple modalities to improve the detection of diseases in cotton leaves. We propose a new methodology utilizing deep learning algorithms that utilize spectral imagery, thermal imagery, and hyper-spectral images to merge their features and thus accurately identify plant diseases. This system allows measuring the plant’s general health to determine the type of disease symptoms occurring on the leaves of various cotton leaf trees in many environments. Hence, the outcomes of extensive experiments using multi-modal images of plants of various kinds – healthy and diseased – show that quantitative tests confirm the ability of the proposed system to detect different types of early cotton leaf diseases with an accuracy, sensitivity, and specificity equal to or better the results published in the literature, which allows the critical impact of the proposed multi-modal deep learning technique to give an early warning against plant diseases before it spreads in the fields, as well as the other numerous uses of its implementation and availability in agricultural papers and applications while aiding the reaching of the goals for further research and developments on the areas to be used by the governing territories where better scalable and accurate disease detection systems would be necessary for such large-scale agricultural needs and real-time surveillance applications.

This paper is intended to explore the research done on identifying the diseased plants and crops using Machine Learning (ML) and Deep Learning (DL) techniques during last 10 years using bibliometric methods. In this study, we used Scopus database to analyze on “Plant disease” or “Crop disease” using “Machine Learning” or “Deep Learning” or “Neural Networks”. This paper focuses on the importance of ML and DL techniques in identifying plant or crop diseases. The database collected from the Scopus is analyzed using VOSviewer software of version 1.6.16. The study is limited to publications from conferences, journals with subject areas are limited to Computer Science, Engineering and languages limited to English and Chinese. Scopus search outputs 824 articles on Plant or Crop diseases with ML, DL and Neural Networks covering conference papers and journal articles. Statistics showed that more articles were published during the last five years and major contributions were from India. By analyzing database on Authors, Subject area, Keywords, Affiliation, Source type it is evident that there is plenty of research scope in this area. Network analysis on diverse parameters specifies that there is a good scope to do research in this topic using advanced deep learning techniques.

Plant diseases have a huge impact on agricultural productivity and global food security. Rapid Identification and timely control of these diseases are crucial in order to reduce yield loss. During recent years deep learning techniques have demonstrated very promising results with regard to automating the detection of plants diseases from leaf images. Transfer learning, one of the popular approaches of deep learning, has been extensively employed to leverage large scale pre trained models and adapt them to new tasks with limited data. This paper presents a study on the application of transfer learning in the detection of plant leaf conditions diseases. A transfer learning approach is proposed where a pre trained convolutional neural. The purpose of this is fine-tuning a CNN model on plant-leaf images for the classification of various diseases.