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In recent years, advancements in computer vision (CV) and machine learning (ML) have facilitated significant progress in the field of plant disease prediction and detection. The growing danger to worldwide food security because of plant diseases necessitates the development of accurate and efficient methods for early disease identification. This paper provides an extensive overview of the current landscape of plant disease prediction using plant images, focusing on datasets, models, and potential future directions. Initially, publicly available datasets that comprise annotated images of healthy and diseased plants, enabling researchers to develop and evaluate predictive models are analyzed. These datasets, encompassing a wide range of crops and diseases, serve as crucial resources for training and benchmarking various algorithms. Also, explore both traditional and modern approaches, including expert systems, ML, and deep learning (DL) algorithms. Model architectures, transfer learning strategies, and ensemble techniques are discussed in terms of their effectiveness in disease classification and localization. This paper also addresses the challenges faced in plant disease prediction, such as data scarcity, model robustness, and scalability. Provide a rendition of the present state of research and identify potential avenues for future exploration, this paper aims to contribute to the advancement of plant disease prediction methods, fostering more resilient and productive agricultural practices. [ABSTRACT FROM AUTHOR]

Plants are acknowledged as being crucial because they are the main source of human energy generation due to their nutritional, therapeutic, and other benefits. Therefore, it is necessary to increase crop productivity. One of these significant factors contributing to reduced agricultural yields is the prevalence of bacterial, fungal, and viral illnesses. Applying techniques for plant disease identification can stop and treat these diseases. So, numerous machine learning (ML) and deep learning (DL) methods were created and tested by researchers to identify plant diseases. Therefore, this study gives a detailed discussion of the various research studies conducted in plant disease detection utilizing ML and DL-based techniques. This review offers research advancements in plant disease recognition from ML to DL techniques. Additionally, many datasets about plant diseases are thoroughly examined. It also addresses the difficulties and issues with the current systems. [ABSTRACT FROM AUTHOR]

With the advancement of deep learning and thermal imaging technology, prediction of plant disease before the appearance of any visual symptoms gains attention. Studies showed that before the appearance of any visual symptoms, some internal changes take place in the plant that cannot be detected externally. These changes may be captured by the thermal images which will help to predict the diseases at the earlier stage. This early prediction will increase the probability and time to recovery; reduce the use of pesticide, resulting in cost effective, quantitative and qualitative production with less environmental pollution. In this study a plant disease prediction system based on thermal images has been developed by exploring the dynamic feature extraction capability of deep learning technology. The proposed system consists of three convolutional layers to overcome the computational overhead and the over fitting problem for small dataset. The system has been tested with a very common disease Bacterial Leaf Blight, of rice plants. The proposed model has been evaluated using several metrics like accuracy, precision, type-I error, type-II error. This novel model predicts the disease at the earliest stage (within 48 h of the inoculation) with 95% accuracy and high precision 97.5% (2.3% Type-I error and 7.7% Type-II error). A comparative study has been done with four standard deep learning models -VGG-16, VGG-19, Resnet50 and Resnet101 and also with machine learning algorithms -Linear Regression and Support Vector Machine, to establish the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Deep learning with convolutional neural networks represents the most used approach in recent years in the classification of leaves' diseases. The literature has extensively addressed the problem using laboratory-acquired datasets with a homogeneous background. In this article, we explore the variability factors that influence the classification of plant diseases by analyzing the same plant and disease under different conditions, i.e., in the field and in the laboratory. Two plant species and five biotic stresses are analyzed using different architectures, such as EfficientB0, MobileNetV2, InceptionV2, ResNet50 and VGG16. Experiments show that model performance drops drastically when using representative datasets, and the features learned from the network to determine the class do not always belong to the leaf lesion. In the worst case, the accuracy drops from 92.67% to 54.41%. Our results indicate that while deep learning is an effective technique, there are some technical issues to consider when applying it to more representative datasets collected in the field. [ABSTRACT FROM AUTHOR]

Deep learning with convolutional neural networks represents the most used approach in recent years in the classification of leaves’ diseases. The literature has extensively addressed the problem using laboratory-acquired datasets with a homogeneous background. In this article, we explore the variability factors that influence the classification of plant diseases by analyzing the same plant and disease under different conditions, i.e., in the field and in the laboratory. Two plant species and five biotic stresses are analyzed using different architectures, such as EfficientB0, MobileNetV2, InceptionV2, ResNet50 and VGG16. Experiments show that model performance drops drastically when using representative datasets, and the features learned from the network to determine the class do not always belong to the leaf lesion. In the worst case, the accuracy drops from 92.67% to 54.41%. Our results indicate that while deep learning is an effective technique, there are some technical issues to consider when applying it to more representative datasets collected in the field.

Every year, plant diseases cause a significant loss of valuable food crops around the world. The plant and crop disease management practice implemented in order to mitigate damages have changed considerably. Today, through the application of new information and communication technologies, it is possible to predict the onset or change in the severity of diseases using modern big data analysis techniques. In this paper, we present an analysis and classification of research studies conducted over the past decade that forecast the onset of disease at a pre-symptomatic stage (i.e., symptoms not visible to the naked eye) or at an early stage. We examine the specific approaches and methods adopted, pre-processing techniques and data used, performance metrics, and expected results, highlighting the issues encountered. The results of the study reveal that this practice is still in its infancy and that many barriers need to be overcome. [ABSTRACT FROM AUTHOR]

Every year, plant diseases cause a significant loss of valuable food crops around the world. The plant and crop disease management practice implemented in order to mitigate damages have changed considerably. Today, through the application of new information and communication technologies, it is possible to predict the onset or change in the severity of diseases using modern big data analysis techniques. In this paper, we present an analysis and classification of research studies conducted over the past decade that forecast the onset of disease at a pre-symptomatic stage (i.e., symptoms not visible to the naked eye) or at an early stage. We examine the specific approaches and methods adopted, pre-processing techniques and data used, performance metrics, and expected results, highlighting the issues encountered. The results of the study reveal that this practice is still in its infancy and that many barriers need to be overcome.

The classification and recognition of foliar diseases is an increasingly developing field of research, where the concepts of machine and deep learning are used to support agricultural stakeholders. Datasets are the fuel for the development of these technologies. In this paper, we release and make publicly available the field dataset collected to diagnose and monitor plant symptoms, called DiaMOS Plant, consisting of 3505 images of pear fruit and leaves affected by four diseases. In addition, we perform a comparative analysis of existing literature datasets designed for the classification and recognition of leaf diseases, highlighting the main features that maximize the value and information content of the collected data. This study provides guidelines that will be useful to the research community in the context of the selection and construction of datasets. [ABSTRACT FROM AUTHOR]