Plant Disease Prediction Research Articles

Plant Diseases Prediction Using Machine Learning

The early detection and accurate prediction of plant diseases are crucial for improving crop health and maximizing agricultural productivity. Traditional methods of disease detection, which rely heavily on manual observation, are often timeconsuming, labor-intensive, and prone to human error. Recent advancements in machine learning (ML) have opened new possibilities for developing efficient, automated systems that can predict plant diseases with high accuracy. This paper explores various machine learning techniques, including supervised and deep learning models, for plant disease prediction. By analyzing features such as leaf texture, color, and environmental data, these models can identify patterns indicative of specific diseases. Several datasets, including real-time image data and environmental metrics (such as humidity, temperature, and soil quality), are utilized to train and evaluate the models. The study focuses on key ML algorithms like Support Vector Machines (SVM), Decision Trees, Random Forests, Convolutional Neural Networks (CNN), and Transfer Learning to understand their efficacy in disease prediction. These models are evaluated based on their accuracy, precision, recall, and computational efficiency. The results demonstrate that deep learning models, particularly CNNs, outperform traditional methods in image-based plant disease identification. In addition, environmental data integration improves the predictive power of ML models, enabling proactive disease management strategies

An Improved Reptile Search Algorithm with Multiscale Adaptive Deep Learning Technique and Atrous Spatial Pyramid Pooling for IoT-based Smart Agriculture Management

Smart farming is an enhanced option for increasing food production, resource management and labour. Existing prediction methods need trained experts to analyse the data, which is a time-consuming process, and thus, there is a need for smart farming with the Internet of Things (IoT). Hence, a well-developed IoT-assisted smart agriculture model is proposed for managing agricultural needs to improve the economic value of farmers. The proposed framework constitutes four major aspects like Crop prediction, Crop yield prediction, Plant disease prediction and Smart irrigation. Initially, the crop images are taken as the input and fed to the Multi-scale Adaptive and Attention-based Convolution Neural Network with Atrous Spatial Pyramid Pooling (MACNN-ASPP), where the dissimilar crops are classified and obtained. In the second aspect, the crop image as well as crop-related data, are fetched from the data sources and given as input. Further, the deep features of the image are extracted that are added with soil and environment condition data. Then this feature is subjected to the Multi-scale Adaptive and Attention-based One-Dimensional Convolution Neural Network with Atrous Spatial Pyramid Pooling (MA1DCNN-ASPP) for crop yield prediction. While in the third aspect, the leaf images are assembled from the data file and fed as input to the MACNN-ASPP for detecting the variety of diseases affecting the plants. In the final aspect, smart irrigation is done by collecting field images with related data. Further, the deep features retrieved from the field images are applied to MA1DCNN-ASPP, where the different conditions of the field will be attained. In order to develop the model adaptively, the hyper-parameters in the network are optimised using the Improved Reptile Search Algorithm (IRSA). Finally, the investigation is done over the proposed methodology using multiple evaluation metrics. In contrast with other approaches, the proposed smart agriculture outperforms the performance of managing crops without any hazardous effects. Accuracy validation executed in the implemented IRSA-MACNN-ASSP-based crop yield prediction model accomplished better efficiency as 6.89%, 4.45%, 2.19% and 1.08% better than the classical techniques like CNN, LSTM, 1DCNN and MA1DCNN-ASPP.

An efficient plant disease prediction model using darner drain fly optimization-based deep convolutional neural network

An efficient plant disease prediction model using darner drain fly optimization-based deep convolutional neural network

EQID: Entangled quantum image descriptor an approach for early plant disease detection

In present day agriculture, early and accurate identification of plant diseases is essential for prompt response, which protects crop quality and output. This paper presents the Entangled Quantum-Inspired Deep learning model (EQID), a unique method that improves feature representation and classification in plant disease prediction by utilizing the concepts of quantum computing. Two different datasets with images of potatoes and tomatoes as leaves were used to test the EQID model, which performed better than traditional models. EQID obtained 98.96% accuracy, 98.98% precision, 98.96% recall, and 98.90% F1 score on images of potato leaves. For tomato leaves, comparable outcomes were noted, with accuracy, precision, recall, and F1 score all above 99.61%. The accuracy of disease prediction is greatly increased by the efficient and effective feature representation made possible by the EQID model's inclusion of quantum computing techniques. Additionally, the model outperformed other cutting-edge models such as DenseNet-121, VGGNet 16, and Xception Net, illustrating the potentially revolutionary effects of quantum-inspired models in agriculture. Future work will focus on applying the EQID model to a broader range of crops and plant diseases, as well as incorporating additional data sources to further enhance the model's predictive capabilities.

Comprehensive Survey on Datasets, Models, and Future Directions in Plant Disease Prediction

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.

Explainable ResNet50 learning model based on copula entropy for cotton plant disease prediction

This paper presents a novel Deep Learning (DL) framework for cotton plant disease prediction based on Explainable Artificial Intelligence (XAI) and Copula entropy based-Grey Wolf Optimization (GWO) algorithm. The suggested framework uses a cotton plant image as input and pre-processes it to produce an image with enhanced contrast. Features are extracted from the leaf images with the ResNet50 CNN model. A crucial pre-processing model known as feature selection helps to improve the effectiveness of image classification by deleting extraneous or irrelevant features. Therefore, the Gray Wolf optimization (GWO) algorithm which is a global search method with potential use in feature selection is employed in this paper. The proposed framework introduces Copula entropy (CE) as an indicator of association to create the GWO’s initial population and enhance the GWO feature engineering process. For the GWO initialization procedure, CE has been used to choose the most significant features which substantially improved the quality of the GWO starting population and as a result improved the performance of the proposed CE-based GWO algorithm by 78.57 % faster than the traditional GWO as stated by the time complexity analysis. In addition, Feature importance explanation is determined using XAI layer. The final classification is achieved using Random Forests (RF) classifier which is an ensemble learning approach. According to the experimental results, the suggested model has a classification accuracy of 99 % and a mean squared error of 0.0383. Furthermore, the proposed model has been compared to state-of-the-art algorithms and the results showed that the proposed model has the superiority performance. The proposed model can therefore be used to track a variety of cotton areas to enable faster analysis and response, resulting in higher productivity.

DPP: A Novel Disease Progression Prediction Method for Ginkgo Leaf Disease Based on Image Sequences

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.

Intelligent Diagnosis and Prediction of Plant Diseases Based on Efficientnet_B3a

Intelligent Diagnosis and Prediction of Plant Diseases Based on Efficientnet_B3a

A hybrid approach for rice crop disease detection in agricultural IoT system

Agriculture is an essential sector that plays a necessary role in the economic improvement of a country. Prediction of plant diseases at the earliest stage may result in better yield and sustainable for growing population. The conventional method necessitates highly skilled inspectors to identify the phenotypic expression of different diseases. Alternatively, biochemical technologies offer more precise means of obtaining crop disease information by analyzing susceptible rice. However, these methods are time-consuming, expensive, reliant on laboratories, and require skilled professionals, rendering them unaffordable for most farmers. The paper aims to propose a solution to prevent infection at the earliest stage for the benefit of farmers. A novel crop disease detection model deploying a deep convolutional generative adversarial network (DC-GAN) and with multidimensional feature compensation Residual Neural Network (MDFC-ResNet) and named as DC-GAN-MDFC–ResNet, which aims at fine grained disease identification system detects from three aspects, bacterial leaf blight, leaf streak and panicle blight. Initially the input data undergone preprocessing using the several processes like data improvement, data normalization, and Singular value decomposition (SVD) to reduce the negative influence that the data set has on the training of the model. When compared to traditional convolution models, the suggested DC-GAN-MDFC–ResNet architecture exhibits in terms of highest classification accuracy, Segmentation free methodology and training stability. The experiments done in this work using Plant Village dataset which show the proposed technique offering improved recognition with the rate of 95.99% accuracy and generating higher quality samples compared to other well-known deep learning models.

Plant disease recognition using residual convolutional enlightened Swin transformer networks

Agriculture plays a pivotal role in the economic development of a nation, but, growth of agriculture is affected badly by the many factors one such is plant diseases. Early stage prediction of these disease is crucial role for global health and even for game changers the farmer’s life. Recently, adoption of modern technologies, such as the Internet of Things (IoT) and deep learning concepts has given the brighter light of inventing the intelligent machines to predict the plant diseases before it is deep-rooted in the farmlands. But, precise prediction of plant diseases is a complex job due to the presence of noise, changes in the intensities, similar resemblance between healthy and diseased plants and finally dimension of plant leaves. To tackle this problem, high-accurate and intelligently tuned deep learning algorithms are mandatorily needed. In this research article, novel ensemble of Swin transformers and residual convolutional networks are proposed. Swin transformers (ST) are hierarchical structures with linearly scalable computing complexity that offer performance and flexibility at various scales. In order to extract the best deep key-point features, the Swin transformers and residual networks has been combined, followed by Feed forward networks for better prediction. Extended experimentation is conducted using Plant Village Kaggle datasets, and performance metrics, including accuracy, precision, recall, specificity, and F1-rating, are evaluated and analysed. Existing structure along with FCN-8s, CED-Net, SegNet, DeepLabv3, Dense nets, and Central nets are used to demonstrate the superiority of the suggested version. The experimental results show that in terms of accuracy, precision, recall, and F1-rating, the introduced version shown better performances than the other state-of-art hybrid learning models.

Machine Learning Models for Plant Disease Prediction and Detection: A Review

In the agriculture field farmers are dependent on crops and once it is caused by disease, they can lose the proper yield. Plant illnesses are the common cause of low yields and reduced income for farmers. So, to overcome this, machine learning approach in the agriculture field is showing exponential improvement for interdisciplinary research, but in modern disease prediction systems, it is a different process that can takes more time to identify and understand the type of disease. Disease prediction is related to computer vision and machine learning to detect types of leaf disease on different plants. It’s a long-term system that includes a variety of real-world scenarios such as, Prediction system for vine leaf disease, pomegranate leaf disease, corn leaf disease, etc. Disease prediction is the accurate determination of the disease state of plant leaves. Accurate disease prediction is one of the key requirements in data science. Presently, machine learning based methods have improved prediction accuracy for plant leafs like grapes, pomegranates, maize. However, the disease prediction performance is still required to be improved in this challenging environment. Existing disease prediction models require high computational time and storage facilities in the agriculture field. To overcome this, we have proposed a comparative study of ML for prediction and classification of crop diseases to improve the efficiency of early prediction of crop diseases.

WECNN-PDP: Weighted Ensemble Convolutional Neural Networks Models to Improve the Plant Disease Prediction

As an agricultural country, Indonesia’s agricultural production is essential. However, crop failure will occur if diseases and other factors, such as natural disasters, attack many plant fields. These problems can be minimized by early detection of plant diseases. However, detection will be challenging if done conventionally. Prior research has shown that deep learning algorithms can perform detection with promising results. In this study, we propose a new weighted deep learning ensemble method as a solution for better performance in plant disease detection. We ensemble the model by considering the combination of two and three pre-trained convolutional neural networks (CNNs). Initially, we perform transfer learning on individual CNN models by prioritizing high-dimensional features through weight updates on the last few layers. Finally, we ensemble the models by finding the best weights for each model using grid search. Experimental results on the Plant Village dataset indicate that our model has improved the classification of 38 plant diseases. Based on metrics, the three-model ensemble performed better than the two-model ensemble. The best accuracy results of the ensemble MobileNetV2-DenseNet121 and MobileNetV2-Xception-DenseNet121 models are 99.49% and 99.56%, respectively. In addition, these models are also better than the state-of-the-art models and previous feature fusion techniques we proposed in LEMOXINET. Based on these results, the ensemble technique improved the detection performance, and it is expected to be applied to real-world conditions and can be a reference to be developed further in future research.

Comparing machine-learning models of different levels of complexity for crop protection: A look into the complexity-accuracy tradeoff

Crop diseases and pests constitute significant causes of yield losses for crops. To limit the harm incurred by those events, farmers resort to plant protection products. Such products are known to have adverse effects both on the environment and on human health. Agronomists make continuous efforts to limit the usage of plant protection products to situations where those products are strictly necessary. To determine such situations, agronomists and policy-makers often rely on decision support tools to model and predict the dynamics of plant diseases. Decision support tools are based either on mechanistic models or on statistical approaches learned from large datasets of biotic (e.g., disease incidence, plant phenological stage) and abiotic (meteorological, soil characteristics) observations in cultures. The surge of powerful machine learning (ML) methods in the last decade makes such approaches a natural pathway to model the dynamics of plant diseases.Machine learning models can reveal the factors that contribute the most to disease and pests outbreaks, provided that those models are simple enough for human inspection. Simplicity, however, may come at the price of lower prediction performances when compared to more complex models.In this paper, we offer a deep look at the performance of ML models of different complexity when used on two use cases of crop disease prediction: downy mildew in the grapevine, and Cercospora leaf spot in the sugar beet.We compare model accuracy and complexity using a year-based cross-validation approach. Our results suggest that interannual meteorological variations are a very important factor in plant disease prediction. Moreover, in line with the observations of the research community in interpretable ML, model complexity stands in clear trade-off with accuracy. This makes models of intermediate complexity appealing for predicting the dynamics of crop diseases as they can provide explicit insights about the rationale of their predictions.

Automated sugarcane crop disease forecasting with colour and texture features

ABSTRACT This paper intends to introduce a novel sugarcane plant disease prediction. There are three stages to the projected method, namely: (i) Preprocessing, (ii) Feature extraction, and (iii) Disease detection phase. Initially, the input image is given to the pre-processing stage in which the grey transformation and bilinear interpolation are carried out. The grey transformation is undergone to improve the clarity of the picture and the bilinear interpolation is used as a re-sampling technique. The feature extraction step follows the pre-processed picture, where both texture and colour features are retrieved. More specifically, the texture features like GLCM and suggested Distance-based LBP (D-LBP) features will be extracted. Subsequently, the extracted features are subjected to the disease detection phase process, where a hybrid classifier is introduced. This hybrid classifier blends the concepts of ‘Neural Network (NN) and Support Vector Machine (SVM)’ for better prediction results. In order to verify the effectiveness of the work that is suggested, a comparison analysis is conducted comparing the suggested and current approaches. The proposed method achieves highest accuracy in 80% of learning which is 13.97%, 16.12%, 11.82%, and 12.9% better than the other methods such as SVM, DT, RF, and NN, respectively.

OPTIMIZING PLANT DISEASE PREDICTION: A NEURO-FUZZY-GENETIC ALGORITHM APPROACH

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-Fuzzy-Genetic 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.

Plant disease prediction system using advance computational Technique

A vital sector of India’s economy is agriculture. Identification of plant infections is crucial to preventing crop damage and further disease. The majority of plants, such as apple, tomato, cherry, and grapes, have leaves that appear to have disease signs. The plant health can be monitored through images to precisely predict the disease and to take early preventative action. The traditional method is to manually inspect the plant leaf to identify the kind of disease, as done by farmers or plant pathologists. In this research, we presented a deep CNN model termed as Decompose, Transfer, and Compose (DTComp) for the classification of plant disease. The deep learning model makes predictions more quickly and precisely than manual plant leaf observation. Out of all the pretrained deep models, the ResNet50 model achieves the highest accuracy for classification. DTComp can handle any anomalies in the images using class decomposition approach to examine the class boundaries. The experimental findings demonstrated DTComp capacity for detecting plant disease instances on dataset gathered from multiple villages using the Kaggel Open Source platform. DTComp can successfully identify plant disease with a high accuracy of 98.30% from images. Additionally, this model can be deployable on real-time systems equipped with a Raspberry Pi and a camera module.

Efficient Prediction of Blast Disease in Paddy Plant using Optimized Support Vector Machine

Diagnostics of plant diseases are essential to predicting yield loss, detecting infections, studying host–pathogen interactions, and detecting host resistance. Early detection of plant leaf disease can increase yields. This is possible if there are automated systems to help farmers diagnose rice diseases from the pictures of plant leaves. Therefore, automatic leaf detection using machine learning is proposed in this study. The proposed approach has three stages, namely, pre-processing, feature extraction, and classification. Initially, the input image is converted into red, green and blue and the noise in the green band is removed using a median filter. Initially, the input image is converted into red, green and blue and the noise in the green band is removed using a median filter. Then, important features of the green band are extracted. An Optimized Support Vector Machine (OSVM) classifier uses the extracted features to classify an image as normal or pathological. To improve SVM performance, the SVM parameters are chosen optimally using the Adaptive Sunflower Optimization (ASFO) algorithm. Then, the infected region is separated using a level-set segmentation algorithm. The efficiency of our work is analyzed based on accuracy, sensitivity, and specificity, the proposed method reached the maximum accuracy of 97.54% for plant disease prediction.

AgriDet: Plant Leaf Disease severity classification using agriculture detection framework

In the field of modern agriculture, plant disease detection plays a vital role in improving crop productivity. To increase the yield on a large scale, it is necessary to predict the onset of the disease and give advice to farmers. Previous methods for detecting plant diseases rely on manual feature extraction, which is more expensive. Therefore, image-based techniques are gaining interest in the research area of plant disease detection. However, existing methods have several problems due to the improper nature of the captured image, including improper background conditions that lead to occlusion, illumination, orientation, and size. Also, cost complexity, misclassifications, and overfitting problems occur in several real-time applications. To solve these issues, we proposed an Agriculture Detection (AgriDet) framework that incorporates conventional Inception-Visual Geometry Group Network (INC-VGGN) and Kohonen-based deep learning networks to detect plant diseases and classify the severity level of diseased plants. In this framework, image pre-processing is done to remove all the constraints in the captured image. Then, the occlusion problem is tackled by the proposed multi-variate grabcut algorithm for effective segmentation. Furthermore, the framework performs accurate disease detection and classification by utilizing an improved base network, namely a pre-trained conventionally based INC-VGGN model. Here, the pre-trained INC-VGGN model is a deep convolutional neural network for prediction of plant diseases that was previously trained for the distinctive dataset. The pre-trained weights and the features learned in this base network are transferred into the newly developed neural network to perform the specific task of plant disease detection for our dataset. In order to overcome the overfitting problem, a dropout layer is introduced, and the deep learning of features is performed using the Kohonen learning layer. After percentage computation, the improved base network classifies the severity classes in the training sets. Finally, the performance of the framework is computed for different performance metrics and achieves better accuracy than previous models. Also, the performance of the statistical analysis is validated to prove the results in terms of accuracy, specificity, and sensitivity.

Plant Disease Prediction in Maize using Convolution Neural Networks in Deep Learning

Plant Disease Prediction in Maize using Convolution Neural Networks in Deep Learning

Machine Learning Model of Plant Disease Prediction: Cotton Leaf Curl Virus (CLCV) on Cotton Crop

The attack of various diseases is one of the major issues in agriculture, and it has a significant impact on the productivity of agriculture. The correlations between the plant disease life cycle and environmental conditions can be successfully used for the prediction and early warnings of the occurrence of diseases. This study proposes a machine-learning approach for the prediction of disease attacks based on environmental conditions. The proposed model is based on the crop field Internet of Things (IoT) based directly sensed temperature, humidity, and rainfall to predict the monthly occurrence of the Cotton Leaf Curl Virus (CLCV) disease on cotton crops. Moreover, the proposed regression model enables the prediction of the CLCV disease attack with a multiple co-efficient of co-relation value of 0.88 and a coefficient of determination (R2) of 0.82 from the prevailing temperature, humidity, and rainfall environmental conditions. The field evaluation reveals that the predicted accuracy of the proposed solution is 85%.