Deep Learning for Plant Disease Detection and Classification: A Systematic Analysis and Review

Objectives: To propose a new model for early detection and classification of apple leaf diseases by means of genetic algorithms and advanced deep learning methods in order to minimize plant degradation and maximize detection accuracy. Methods: A feature selection and extraction task is carried out by using an Improvised Flower Pollination Technique (IFPA) based genetic algorithm. Deep learning techniques such as Multi Class Support Vector Machine (MC-SVM) and Spectral Vegetation Indices (SVI) are used to classify and detect disease. The Region Growing Algorithm (RGA) and the Stochastic Gradient Descent Method (SGDM) are used to detect leaf colour segments and identify the disease at an early stage based on shape, texture, and colour features. IFPA-GA with MC-SVM and SVI extracts the features from the apple leaf dataset (collected from the Apple Experiment Station of Northwest A&F University, China) and then selects the appropriate function to perform classification and detection of disease at early occurrence and to improve the accuracy level. To demonstrate the efficiency of the proposed algorithm, MATLAB is used for implementation. The performance results are evaluated and compared to the existing models such as Faster R-CNN, R-SSD, and INARSSD. Findings: Early disease detection and classification of leaf diseases are achieved with 94.09% accuracy level, 93.07% Speed, 94.01% sensitivity, 93.38% specificity, 95.01% precision, 93.17% recall, 190 True Positive, 110 True Negative, and 92.07% F-Score to detect the disease and classify it in an optimized manner, which is high compared to the existing versions. Novelty: According to the findings of the comprehensive study, the proposed detection and classification method IFPA-GA with SVM-SVI outperforms Faster R-CNN, R-SSD, and INAR-SSD in terms of accuracy and speed of apple leaf disease detection at an early occurrence by classifying it in a robust manner. Keywords: Apple Leaf Disease Detection; SVM; Genetic Algorithm; Deep Learning; Image Processing

Tomato plants' disease detection and classification at the earliest stage can save the farmers from expensive crop sprays and can assist in increasing the food quantity. Although, extensive work has been presented by the researcher for the tomato plant disease classification, however, the timely localization and identification of various tomato leaf diseases is a complex job as a consequence of the huge similarity among the healthy and affected portion of plant leaves. Furthermore, the low contrast information between the background and foreground of the suspected sample has further complicated the plant leaf disease detection process. To deal with the aforementioned challenges, we have presented a robust deep learning (DL)-based approach namely ResNet-34-based Faster-RCNN for tomato plant leaf disease classification. The proposed method includes three basic steps. Firstly, we generate the annotations of the suspected images to specify the region of interest (RoI). In the next step, we have introduced ResNet-34 along with Convolutional Block Attention Module (CBAM) as a feature extractor module of Faster-RCNN to extract the deep key points. Finally, the calculated features are utilized for the Faster-RCNN model training to locate and categorize the numerous tomato plant leaf anomalies. We tested the presented work on an accessible standard database, the PlantVillage Kaggle dataset. More specifically, we have obtained the mAP and accuracy values of 0.981, and 99.97% respectively along with the test time of 0.23 s. Both qualitative and quantitative results confirm that the presented solution is robust to the detection of plant leaf disease and can replace the manual systems. Moreover, the proposed method shows a low-cost solution to tomato leaf disease classification which is robust to several image transformations like the variations in the size, color, and orientation of the leaf diseased portion. Furthermore, the framework can locate the affected plant leaves under the occurrence of blurring, noise, chrominance, and brightness variations. We have confirmed through the reported results that our approach is robust to several tomato leaf diseases classification under the varying image capturing conditions. In the future, we plan to extend our approach to apply it to other parts of plants as well.

India has a huge population and the major source of food supply is from agriculture. Agricultural lands are getting destroyed mostly due to crop diseases, pests and plant diseases. Nowadays it increased fiercely. Plant pathogens are fungi, bacteria, virus or nematodes that damage plant parts such as leaf, panicle, node, stem and roots. So In agriculture, one of the latest researches is detection and classification of crop diseases using images of leaves from plants. The detection of plant diseases by using the image processing methods can help the farmers to protect the agricultural field from getting destroyed or affected. Paddy is a staple crop for much of the world's population. In this research we discuss classification and detection of paddy Leaf diseases using convolutional neural network. We captured paddy leaf images from the field for normal, sheath rot, rice blast, bacterial leaf blight, rice blast, brown spot, rymv and rice tungro for processing the image. In pre-processing, background exclusion is done based on the hue value to extract the non-diseased and diseased part. Feature Extraction is executed by applying Convolutional Neural Network. The proposed system achieved a high accuracy.KeywordsCrop diseasesLeavesPre-processingFeature extractionDeep learning modelConvolutional Neural Network

Detection of plant leaf diseases using image segmentation and soft computing techniques

Presently, rapid and precise disease identification process plays a vital role to increase agricultural productivity in a sustainable manner. Conventionally, human experts identify the existence of anomaly in plants occurred due to disease, pest, nutrient deficient, weather conditions. Since manual diagnosis process is a tedious and time consuming task, computer vision approaches have begun to automatically detect and classify the plant diseases. The general image processing tasks involved in plant disease detection are preprocessing, segmentation, feature extraction and classification. This paper performs a review of computer vision based plant disease detection and classification techniques. The existing plant disease detection approaches including segmentation and feature extraction techniques have been reviewed. Additionally, a brief survey of machine learning (ML) and deep learning (DL) models to identify plant diseases also takes place. Furthermore, a set of recently developed DL based tomato plant leaf disease detection and classification models are surveyed under diverse aspects. To further understand the reviewed methodologies, a detailed comparative study also takes place to recognize the unique characteristics of the reviewed models.

A review on machine learning and deep learning image-based plant disease classification for industrial farming systems

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.

India has a vast number of inhabitants and the main food source distribution is from agriculture. Agricultural lands are being demolished generally owing to plant and crop illnesses. The detection of plant diseases by using image processing models can aid agriculturalists in defending the farming area from damaging or affecting it. Paddy is the main harvest worldwide. Early recognition of the paddy diseases at dissimilar phases of development is very vital in paddy production. However, the present manual technique in identifying and classifying paddy diseases needs a very educated farmer and is time-consuming. Deep learning (DL) is an effectual research area in the classification of agriculture patterns where it can efficiently solve the problems of diseases identification. Therefore, the articles focus on the design and expansion of Deep Learning based Crested Porcupine Optimizer for the Detection and Classification of Paddy Leaf Diseases (DLCPO-DCPLD) method for Sustainable Agriculture. The main aim of the DLCPO-DCPLD method use DL method for the recognition and identification of rice plant leaf diseases. To accomplish this, the DLCPO-DCPLD technique performs the image pre-processing using Median Filtering (MF) to recover the excellence of the input frames. Next, the ConvNeXt-L method is applied for extraction of feature vectors from the pre-processed images. Also, the Conditional Variational Autoencoder (CVAE) model is utilized for the automated classification of Paddy Leaf diseases. Eventually, the hyperparameter tuning of the CVAE technique is accomplished by implementing the Crested Porcupine Optimizer (CPO) technique. To safeguard the enhanced predictive results of the DLCPO-DCPLD method, a sequence of experimentations is implemented on the benchmark dataset. The experimental validation of the DLCPO-DCPLD method portrayed a superior accuracy value of 99.12% over existing approaches.

H2K – A robust and optimum approach for detection and classification of groundnut leaf diseases

In recent years plant disease detection and classification is finding a lot of scope in the field of agriculture. The use of image pre-processing along with deep learning techniques is making the role of farmers easy in the process of plant leaf disease detection. In this paper we propose a deep learning technique, ResNet-50 for the identification and classification of leaf diseases mainly in banana and sunflower. Images for the training and testing purpose are collected by visiting the farms and from village dataset for normal, leaf spot, leaf blight, powdery mildew, bunchy top, sigatoka, panama wilt. Pre-processing is done to remove eliminate the noise in the image by converting the RGB input to HSV image. Binary pictures are retrieved to separate the diseased and unaffected portions based on the hue and saturation components. A clustering method is utilized to separate the diseased region from the normal portion and the background. Classification of the disease is carried out using ResNet-50 algorithm. The experimental results obtained are compared with CNN, machine learning algorithms like SVM, KNN, DT and Ensemble algorithm like RF and XG booster. The proposed algorithm provided maximum efficiency compared to other algorithms.

Accurate and effective classification of plant diseases is critical for increasing yield and quality in agricultural production, minimizing economic losses through early detection of diseases, and implementing sustainable agriculture approaches. In this study, an approach for the detection and classification of plant leaf diseases is presented. We compare the performance of machine learning and deep learning-based models, and we use GAN-based data synthesis methods on a dataset we created to improve the model performance. For machine learning methods, ResNet-based feature extraction is performed and XGBoost, Random Forest, SVM, and InceptionV3 models are evaluated. In contrast, AlexNet, VGG16, VGG19, DenseNet, and ResNet models are examined within the scope of deep learning. The study was analyzed in three classes Phytophthora Infestans, Potassium Deficiency, and Healthy, and tested on data obtained from 21 different plant species. According to the model performances obtained, the deep learning-based ResNet model showed the highest success in all performance metrics and achieved 99% accuracy, showing superior performance compared to other methods. In the study, a comprehensive evaluation of multiple classification, GAN-based data synthesis, machine learning, and deep learning models was carried out. A valuable contribution was made to the existing studies in the literature.

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 and pest range assessment, etc. 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 paper, 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 work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved.

Agriculture is the primary source of livelihood for about 60% of the world's total population according to the Food and Agricultural Organization (FAO). The economy of the developing countries is solely dependent on agriculture commodities. As the world population is increasing at faster pace, the demand for food is also escalating tremendously. In recent days, agriculture is experiencing an automation revolution. Hence the introduction of disruptive technologies like Artificial Intelligence plays a major role in increasing agricultural productivity. AI enabled approaches would help in overcoming the traditional challenges faced in agriculture practices, by automating various agriculture related tasks. Nowadays, farmers adopt precision farming which uses AI techniques namely in crop health monitoring, weed detection, plant disease identification and detection, and forecast weather, commodity prices to increase the yield. As there is scarcity of manpower in agriculture sector, AI based equipment like bots and drones are used widely. Crop diseases are a major threat to food security and the manual identification of the diseases with the help of experts will incur more cost and time, especially for larger farms. The machine-vision based techniques provide image based automatic process control, inspection, and robot guidance for pest and disease control. It provides automated process in agriculture, paving way for improved efficiency and profitability. Various factors contribute for plant diseases, which includes soil health, climatic conditions, species and pests. The proposed chapter elaborates on the use of deep learning techniques in the leaf disease detection of Cassava plants. The chapter initially describes the evolution of various neural network techniques used in classification and prediction. It describes the significance of using Convolutional Neural Network (CNN) over deep neural networks. The chapter focuses on classification of leaf disease in Cassava plants using images acquired real time and from Kaggle dataset. In the final part of the chapter, the results of the models with original and augmented data were illustrated considering accuracy as performance metric.

Benefits of independent learning and extraction of features have received a lot of attention in recent years from both academic and professional circles. A subcategory of artificial intelligence is deep learning. The use of deep learning towards plant disease recognition can prevent the drawbacks associated with crop disease and production losses. In order to identify and characterize the signs of plant diseases, numerous established machine learning and deep learning architectures are used in conjunction with a number of visualization tools. The detection of leaf disease using image processing has been covered in this survey. Leaf disease diagnosis is enhanced when image segmentation is used in combination with deep learning or machine learning models. A big data collection can be segmented with the use of image segmentation, and the output is then fed to the AI algorithms on disease detection. Additionally, this survey covers the performance metrics of prior studies, which offered guidance for future advancements in plant disease detection and prevention methods.

Subsistence farmers and global food security depend on sufficient food production, which aligns with the UN's “Zero Hunger,” “Climate Action,” and “Responsible Consumption and Production” sustainable development goals. In addition to already available methods for early disease detection and classification facing overfitting and fine feature extraction complexities during the training process, how early signs of green attacks can be identified or classified remains uncertain. Most pests and disease symptoms are seen in plant leaves and fruits, yet their diagnosis by experts in the laboratory is expensive, tedious, labor-intensive, and time-consuming. Notably, how plant pests and diseases can be appropriately detected and timely prevented is a hotspot paradigm in smart, sustainable agriculture remains unknown. In recent years, deep transfer learning has demonstrated tremendous advances in the recognition accuracy of object detection and image classification systems since these frameworks utilize previously acquired knowledge to solve similar problems more effectively and quickly. Therefore, in this research, we introduce two plant disease detection (PDDNet) models of early fusion (AE) and the lead voting ensemble (LVE) integrated with nine pre-trained convolutional neural networks (CNNs) and fine-tuned by deep feature extraction for efficient plant disease identification and classification. The experiments were carried out on 15 classes of the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 categories. Hyperparameter fine-tuning was done with popular pre-trained models, including DenseNet201, ResNet101, ResNet50, GoogleNet, AlexNet, ResNet18, EfficientNetB7, NASNetMobile, and ConvNeXtSmall. We test these CNNs on the stated plant disease detection and classification problem, both independently and as part of an ensemble. In the final phase, a logistic regression (LR) classifier is utilized to determine the performance of various CNN model combinations. A comparative analysis was also performed on classifiers, deep learning, the proposed model, and similar state-of-the-art studies. The experiments demonstrated that PDDNet-AE and PDDNet-LVE achieved 96.74% and 97.79%, respectively, compared to current CNNs when tested on several plant diseases, depicting its exceptional robustness and generalization capabilities and mitigating current concerns in plant disease detection and classification.