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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]

Ginkgo leaf disease poses a grave threat to Ginkgo biloba. The current management of Ginkgo leaf disease lacks precision guidance and intelligent technologies. To provide precision guidance for disease management and to evaluate the effectiveness of the implemented measures, the present study proposes a novel disease progression prediction (DPP) method for Ginkgo leaf blight with a multi-level feature translation architecture and enhanced spatiotemporal attention module (eSTA). The proposed DPP method is capable of capturing key spatiotemporal dependencies of disease symptoms at various feature levels. Experiments demonstrated that the DPP method achieves state-of-the-art prediction performance in disease progression prediction. Compared to the top-performing spatiotemporal predictive learning method (SimVP + TAU), our method significantly reduced the mean absolute error (MAE) by 19.95% and the mean square error (MSE) by 25.35%. Moreover, it achieved a higher structure similarity index measure (SSIM) of 0.970 and superior peak signal-to-noise ratio (PSNR) of 37.746 dB. The proposed method can accurately forecast the progression of Ginkgo leaf blight to a large extent, which is expected to provide valuable insights for precision and intelligent disease management. Additionally, this study presents a novel perspective for the extensive research on plant disease prediction. [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]

Smart farming is revolutionizing agriculture by integrating advanced technologies to enhance productivity, efficiency, and sustainability. This paper proposes a novel, 5G-enabled Pest and Disease Detection and Response System (PDDRS) that synergizes environmental sensor data with image analytics for comprehensive Plant Disease Detection (PDD). By leveraging the high bandwidth and ultra-low latency capabilities of 5G, our integrated system surpasses traditional communication technologies, facilitating real-time data analytics and immediate intervention strategies. We introduce two Machine Learning (ML) models: an image-based Mask R-CNN with FPN, which achieves a precision of 91.1% and an accuracy of 95.1%, and an environmental-based FFNN + LSTM model, evaluated for ACC, AUC, and F1-Score, showing promising results in disease forecasting. Our experiments demonstrate that the PDDRS significantly enhances throughput and latency performance under various connected devices, showcasing a scalable, cost-effective solution suitable for next-generation smart farming. These advancements collectively empower the PDDRS to deliver actionable insights, enabling targeted applications such as precise pesticide deployment, and stand as a testament to the potential of 5G in agricultural innovation. [ABSTRACT FROM AUTHOR]

Ginkgo leaf disease poses a grave threat to Ginkgo biloba. The current management of Ginkgo leaf disease lacks precision guidance and intelligent technologies. To provide precision guidance for disease management and to evaluate the effectiveness of the implemented measures, the present study proposes a novel disease progression prediction (DPP) method for Ginkgo leaf blight with a multi-level feature translation architecture and enhanced spatiotemporal attention module (eSTA). The proposed DPP method is capable of capturing key spatiotemporal dependencies of disease symptoms at various feature levels. Experiments demonstrated that the DPP method achieves state-of-the-art prediction performance in disease progression prediction. Compared to the top-performing spatiotemporal predictive learning method (SimVP + TAU), our method significantly reduced the mean absolute error (MAE) by 19.95% and the mean square error (MSE) by 25.35%. Moreover, it achieved a higher structure similarity index measure (SSIM) of 0.970 and superior peak signal-to-noise ratio (PSNR) of 37.746 dB. The proposed method can accurately forecast the progression of Ginkgo leaf blight to a large extent, which is expected to provide valuable insights for precision and intelligent disease management. Additionally, this study presents a novel perspective for the extensive research on plant disease prediction.

In this essay, the idea of improving plant disease prediction using a Neuro-Fuzzy-Genetic algorithm (NFGA) technique is explored. The concept of a Neuro-Fuzzy-Genetic algorithm is first described. The advantages of the NFGA technique for predicting plant disorders are next addressed. An example is then given to demonstrate how this technique has been successfully used and the advantages it offers you. A hybrid artificial intelligence technique known as a Neuro-FuzzyGenetic set of rules (NFGA) combines the genetic algorithms, fuzzy logic, and neural network algorithms. It entails using a method of organizing fuzzy rules for statistics type, developing a network of neurons to anticipate the level of the group of data points to positive fuzzy training, adjusting the weights of fuzzy classes using a genetic algorithm-based totally optimization method to better fit the data factors, and ultimately identifying and predicting patterns of statistics points. The main benefits of this method for predicting plant diseases are its abilities to analyze various plant characteristics, extract complex relationships between statistics points, identify correlations between various environmental factors and illnesses, choose the best combinations of fuzzy rules for accurate classification, and finally adapt to changing data over time. [ABSTRACT FROM AUTHOR]

Smart agriculture and plant disease prediction have become important areas of research due to the increasing demand for food production and the need for sustainable farming practices. The paper provides a review of smart agricultural practises and how machine learning algorithms are used to predict plant diseases. Smart agriculture is based on the use of technology such as the Internet of Things (IoT) which enable farmers to monitor and manage their farms remotely. Additionally, machine learning algorithms can be used to accurately identify plant diseases through analysing the input plant image. Three machine learning algorithms have been used in this paper i.e. SVM, VGG19, and KNN. VGG16 is used for the feature extraction. With the 13 different types of diseases, highest accuracy system achieved is 99%. [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]

The performance of meteorological data-based methods to forecast plant diseases strongly depends on temporal weather information. In this paper, a data analysis procedure is presented for finding the optimal starting time for forecasting net blotch density in spring barley based on meteorological data. For this purpose, changes in the information content of typically measured weather variables were systemically quantified in sliding time windows and with additionally generated mathematical transformations, namely with features. Signal-to-noise statistics were applied in a novel way as a metric for identifying the optimal starting time instance and the most important features to successfully distinguish between two net blotch densities during springtime itself. According to the results, the information content of meteorological data used in classifying between nine years with and four years without net blotch reached its maximum in Finnish weather conditions on the 41st day from the beginning of the growing season. Specifically, utilising weather data at 41–55 days from the beginning of the growing season maximises successful forecasting potential of net blotch density. It also seems that this time instance enables a linear classification task with a selected feature subset, since the averages of the metrics in two data groups differ statistically with a minimum 68% confidence level for nine days in a 14-day time window.

With the aid of a plant disease forecasting model, the emergence of plant diseases in a given region can be predicted ahead of time. This makes it easier to take proactive steps to reduce losses before they occur. The proposed model attempts to find an association between agrometeorological parameters and the occurrence of the four types of rice diseases. Rice is the staple food of people in Maharashtra. The four major diseases that occur on rice crops are focused on this paper (namely Rice Blast, False Smut, Bacterial Blight and Brown Spot) as these diseases spread rapidly and lead to economic loss. This research paper demonstrates the usage of artificial neural network (ANN) to detect, classify and predict the occurrence of rice diseases based on diverse agro-meteorological conditions. The results were carried out on two cases of dataset split that is 70–30% and 80–20%. The various types of activation function (AF) such as sigmoid, tanH, ReLU and softmax are implemented and compared based on various evaluation metrics such as overall Accuracy, Precision, Recall and F1 score. It can be concluded that the softmax AF applied to 70–30% split of dataset gives the highest accuracy of 92.15% in rice disease prediction.

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]

Plasmopara viticola (Peronosporales: Peronosporaceae) is a highly destructive disease that can cause very serious damage under favorable conditions. Cultural practices, disease-tolerant varieties, and phytosanitary treatments have been effective tools to control this disease. However, predictive models such as decision support systems (DSS) could help reduce the need for phytosanitary treatments. In this study, we aimed to develop, adjust, and validate a customized predictive model at the plot level, comparing it with the other three existing models (Milvit, VitiMeteo-Plasmopara, and Goidanich) in Mediterranean conditions. Downy mildew symptoms were observed in the field and predicted using the four mentioned models in eight plots during the 2018 and 2019 campaigns. All tested models predicted possible daily infection cycles, which were grouped into infection blocks every 5–7 days to validate predicted data with in-field observations. In Mediterranean conditions, Milvit and VitiMeteo-Plasmopara models provided both over-prediction and under-prediction infection blocks compared to field observations. However, the Goidanich model only provided over-prediction infection blocks with very few matches to field observations. The UR-model was successfully improved to adjust it to Mediterranean conditions, and it matched observed and predicted infection blocks in 56,25% of the plots and years. However, the UR-model was more accurate (75% matching) in humid years like 2018 than in dryer years like 2019 (37,5% matching), although in both cases it was the most accurate model to be used as a decision support system. • 1 customized model, and 3 existing ones, were tested in Mediterranean conditions. • Predicted infection cycles were grouped into infection blocks of 5–7 days. • Milvit and VitiMeteo models showed too many under-predictions and over-predictions. • Goidanich model showed 37,5% matching with in-field observations. • UR-model provided highest matching, 56,25%, with in-field observed symptoms. [ABSTRACT FROM AUTHOR]

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.

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.

A novel data analysis method for the evaluation of plant disease risk that utilizes weather information is presented in this paper. This research considers two different datasets: open weather data from the Finnish Meteorological Institute and long-term (1991–2017) plant disease severity observations in different hardiness zones in Finland. Historical net blotch severity data on spring barley were collected from official variety trials carried out by the Natural Resources Institute Finland (Luke) and the analysis was performed with existing data without additional measurements. Feature generation was used to combine different datasets and to enrich the information content of the data. The t-test was applied to validate features and select the most suitable one for the identification of datasets with high net blotch risk. Based on the analysis, the selected daily measured variables for the estimation of net blotch density were the average temperature, minimum temperature, and rainfall. The results strongly indicate that thorough data analysis and feature generation methods enable new tools for plant disease prediction. This is crucial when predicting the disease risk and optimizing the use of pesticides in modern agriculture. Here, the developed system resolves the correlation between weather measurements and net blotch observations in a novel way.

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]

A novel data analysis method for the evaluation of plant disease risk that utilizes weather information is presented in this paper. This research considers two different datasets: open weather data from the Finnish Meteorological Institute and long-term (1991–2017) plant disease severity observations in different hardiness zones in Finland. Historical net blotch severity data on spring barley were collected from official variety trials carried out by the Natural Resources Institute Finland (Luke) and the analysis was performed with existing data without additional measurements. Feature generation was used to combine different datasets and to enrich the information content of the data. The t-test was applied to validate features and select the most suitable one for the identification of datasets with high net blotch risk. Based on the analysis, the selected daily measured variables for the estimation of net blotch density were the average temperature, minimum temperature, and rainfall. The results strongly indicate that thorough data analysis and feature generation methods enable new tools for plant disease prediction. This is crucial when predicting the disease risk and optimizing the use of pesticides in modern agriculture. Here, the developed system resolves the correlation between weather measurements and net blotch observations in a novel way. [ABSTRACT FROM AUTHOR]