Crop Disease Prediction Research Articles

Cassava crop disease prediction and localization using object detection

In agriculture, early detection and localization of plant diseases in time using deep learning techniques can help farmers contain the spread of plant diseases. In this work, we apply object detection models to identify and localize various categories of cassava plant leaf diseases. These include You Only Look Once (YOLO) as well as Generalized Efficient Layer Aggregation Network(GELAN) models. We applied YOLO v9-e, YOLO v9-c, as well as GELAN-e and GELAN-c models. The models were successfully trained using a custom cassava dataset. Several evaluation indicators that include precision, recall and mean average precision(mAP) were analysed. The results have been compared with an earlier version of YOLO model and show an improvement in evaluation indicators reaching above 80% in the majority of diseases.

Integrating IoT for Soil Monitoring and Hybrid Machine Learning in Predicting Tomato Crop Disease in a Typical South India Station.

This study develops a hybrid machine learning (ML) algorithm integrated with IoT technology to improve the accuracy and efficiency of soil monitoring and tomato crop disease prediction in Anakapalle, a south Indian station. An IoT device collected one-minute and critical soil parameters-humidity, temperature, pH values, nitrogen (N), phosphorus (P), and potassium (K), during the vegetative growth stage, which are essential for assessing soil health and optimizing crop growth. Kendall's correlations were computed to rank these parameters for utilization in hybrid ML techniques. Various ML algorithms including K-nearest neighbors (KNN), support vector machines (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were evaluated. A novel hybrid algorithm, 'Bayesian optimization with KNN', was introduced to combine multiple ML techniques and enhance predictive performance. The hybrid algorithm demonstrated superior results with 95% accuracy, precision, and recall, and an F1 score of 94%, while individual ML algorithms achieved varying results: KNN (80% accuracy), SVM (82%), DT (77%), RF (80%), and LR (81%) with differing precision, recall, and F1 scores. This hybrid ML approach proved highly effective in predicting tomato crop diseases in natural environments, underscoring the synergistic benefits of IoT and advanced ML techniques in optimizing agricultural practices.

A Comprehensive Survey on Machine Learning and Deep Learning Techniques for Crop Disease Prediction in Smart Agriculture

Diseases caused by bacteria, fungi, and viruses are a problem for many crops. Farmers have challenges when trying to evaluate their crops daily by manual inspection across all forms of agriculture. Also, it is difficult to assess the crops since they are affected by various environmental factors and predators. These challenges can be addressed by employing crop disease detection approaches using artificial intelligence-based machine learning and deep learning techniques. This paper provides a comprehensive survey of various techniques utilized for crop disease prediction based on machine learning and deep learning approaches. This literature review summarises the contributions of a wide range of research works to the field of crop disease prediction, highlighting their commonalities and differences, parameters, and performance indicators. Further, to evaluate, a case study has been presented on how the paradigm shift will lead us to the design of an efficient learning model for crop disease prediction. It also identifies the gaps in knowledge that are supposed to be addressed to forge a path forward in research. From the survey conducted, it is apparent that the deep learning technique shows high efficiency over the machine learning approaches, thereby preventing crop loss.

Crop Disease Prediction Using Web Application

Abstract: Agriculture plays a very vital role in our life. Without agriculture, the existence of human beings is not possible as it is the main source of our food supply to sustain on the earth and it also helps to grow our economy across the world. Plant disease detection is one of the most important aspects of maintaining an agriculturally developed nation. The timely and efficient detection of plant diseases is essential for a healthy and productive agricultural sector. Various diseases like Common Rust, Bacterial Spot, Leaf Mold, Mosaic Virus, Powdery Mildew and others that could affect a plant and cause farmer to lose a substantial sum yearly. Deep Learning (DL) can play a crucial role in helping farmers to prevent crop failure by early disease detection in plant leaves. In the experiment, examination conducted by Convolution Neural Network (CNN) model on dataset (which consists of images of healthy and infected leaves) to detect plant diseases and Web Application is used for real-life crop disease prediction. The proposed Web Application aims to assist farmers in detection of plant diseases by analyzing images of the plant leaves. The proposed application uses the CNN model to distinguish healthy and infected leaves. The goal is to help farmers and prevent economic loss by detecting plant diseases early

AgriSolutions: Innovations in Crop Disease Prediction, Crop Recommendation, and E Commerce

Abstract: Crop disease prediction and crop recommendation are important steps towards sustainable agriculture. The use of deep learning, EfficientNet_v2 and image processing technologies can help in improve accuracy in these tasks. Identification of plant diseases and recommendation of crops cannot maximize only yield production but can be supportive for varied types of agricultural practices The existing system of crop recommendation employs data analysis to suggest suitable crops based on soil and climate factors, while disease prediction system utilizes predictive models to anticipate and prevent crop diseases. In this Paper, we propose a model that combines these techniques to accurately identify to recommend suitable crops based on the Soil Parameters and climate conditions of the farm. Our proposed model achieved and classify plant diseases, recommend suitable crops based on the soil condition. To achieve this, we trained a deep neural network on a large dataset of images of healthy and diseased plants, and performed image preprocessing.

Rootrise - Make Automate Agriculture (Crop Prediction, Crop Disease Detection and Fertilizer Recommendation)

Abstract: Agricultural productivity and sustainability are vital for global food security, but challenges like soil degradation, crop diseases, and inefficient fertilizer use hinder crop quality and yield. To address these, advanced technologies like machine learning and AI are increasingly used in agriculture. This review focuses on recent advances in soil prediction, crop disease prediction, and fertilizer recommendation systems.

BAT Algorithm-Based Multi-Class Crop Leaf Disease Prediction Bootstrap Model

In the task of identification of infected agriculture plants, the leaf-based disease identification technique is especially effective in better understand crop disease among various techniques to detect infection. Recognition of an infected leaf image from healthy images gets encumbered when the model is required to detect the type of leaf disease. This paper presents a BAT-based crop disease prediction bootstrap model (BCDPBM) that identifies the health of the leaf and performs disease prediction. The BAT algorithm in the proposed model increases the capability of the Gaussian mixture model for foreground region detection. Furthermore, in the work, the co-occurrence matrix feature and histogram feature are extracted for the training of the bootstrap model. Hence, leaf foreground detection by the BAT algorithm with the Gaussian mixture improves the feature extraction quality for bootstrap learning. The proposed model utilizes a dataset of real leaf images for conducting experiments. The results of the model are compared with different existing models across various parameters. The results show the prediction accuracy enhancement of multiclass leaf disease using the BCDPBM model.

An efficient IoT based crop disease prediction and crop recommendation for precision agriculture

An efficient IoT based crop disease prediction and crop recommendation for precision agriculture

Intelligence assistant using deep learning: use case in crop disease prediction

Intelligence assistant using deep learning: use case in crop disease prediction

Effective IoT-based crop disease prediction using localise search traversing coupled with deep convolutional neural network classifier

Effective IoT-based crop disease prediction using localise search traversing coupled with deep convolutional neural network classifier

Enhancing crop disease prediction in tropical regions through GAN-based transfer learning

Enhancing crop disease prediction in tropical regions through GAN-based transfer learning

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.

A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

Crop pests reduce productivity, so managing them through early detection and prevention is essential. Data from various modalities are being used to predict crop diseases by applying machine learning methodology. In particular, because growth environment data is relatively easy to obtain, many attempts are made to predict pests and diseases using it. In this paper, we propose a model that predicts diseases through previous growth environment information of crops, including air temperature, relative humidity, dew point, and CO2 concentration, using deep learning techniques. Using large-scale public data on crops of strawberry, pepper, grape, tomato, and paprika, we showed the model can predict the risk score of crop pests and diseases. It showed high predictive performance with an average AUROC of 0.917, and based on the predicted results, it can help prevent pests or post-processing. This environmental data-based crop disease prediction model and learning framework are expected to be universally applicable to various facilities and crops for disease/pest prevention.

Image based Chili Crop Disease Prediction Using Deep Transfer Learning

Crop diseases have a terrible impact on food protection and can result in considerable reductions in both the supply and quality of agricultural products. Human professional have traditionally been relying on to diagnose crop diseases caused by insects, pests, virus, bacteria, fungal, inadequate nutrition, or adverse environmental conditions. This, however, is costly, time demanding, and in some situations unworkable. Thus, in the area of agricultural information, the automatic identification of crop diseases is significantly required. Many strategies have been presented to solve this challenge, with deep learning becoming as the preferred approach due to its outstanding performance.This research describes a method for detecting chili leaf diseases using a deep convolutional neural network. We compared performances of four architectures: MobileNet, Inception-ResnetV2, EfficientNetB0, and DenseNet. The proposed approach evaluated the findings using measures such as accuracy, loss and time. Our model compares favorably to EfficientNetB0 with an accuracy of 0.995, a loss of 0.023, and time is 5 minutes 45 seconds. EfficientNetB0, a compact deep learning architecture has fine tuned to classify two forms of chili leaf diseases. The method was tested on 2475 photos from the Plant Village dataset.

DeepCrop: Deep learning-based crop disease prediction with web application

Agriculture plays a significant role in every nation's economy by producing crops. Plant disease identification is one of the most important aspects of maintaining an agriculturally developed nation. The timely and efficient detection of plant diseases is essential for a healthy and productive agricultural sector and to prevent wasting money and other resources. Various diseases that could affect a plant cause crop farmers to lose a substantial sum yearly. Deep learning can play a crucial role in helping farmers prevent crop failure by early disease detection in plant leaves. In the experiment, we examined CNN, VGG-16, VGG-19 and ResNet-50 models on plant-village 10000 image dataset to detect crop infection and got the accuracy rate of 98.60%, 92.39%, 96.15%, and 98.98% for CNN, VGG-16, VGG-19 and ResNet-50 respectively. The study indicates that ResNet-50 outperforms the other models with an accuracy of 98.98%. So, the ResNet50 model was chosen to be developed into a smart web application for real-life crop disease prediction. The proposed web application aims to assist farmers in identifying diseases of plants by analyzing photos of the plant leaves. The proposed application uses the ResNet50 transfer learning model at its heart to distinguish healthy and infected leaves and classify the present disease type. The goal is to help farmers save resources and prevent economic loss by detecting plant diseases early and applying the appropriate treatment.

Effective IoT-based crop disease prediction using localised search traversing coupled with deep convolutional neural network classifier

Effective IoT-based crop disease prediction using localised search traversing coupled with deep convolutional neural network classifier

Fog Data Processing and Analytics for Agriculture IoT Data Streams

Pervasiveness rise of smart devices and sensor-based gadgets in building IoT systems is increasing unprecedented in technological innovations. The emergence of the Internet of Things technologies reshaped nearly every sector including agriculture. The agriculture sector contributes a significant figure to the country’s economy and it has a wide-ranging involvement in the advancement of human civilization. In the current scenario, the proper techniques of farming are needed as utmost propriety for better crop quality and quantity in a high-competition market. Crop disease prediction is key to shattering the problems of the farmer, reducing the usage of insecticides, and pesticides, and improving the financial conditions of the farmer. The Internet of Things and data analytics possess the ability to positively modernize the agricultural sector. However, Internet of Things-based applications needed to be deployed on a platform that offers real-time experience, reduced latency, and optimal bandwidth usage. Fog computing extends the computational power closer to the edge network where the devices reside and facilitate edge intelligence. In this paper, the fog importance, fog computing architecture along with the perceptions of data analysis in a fog environment, and emerging research challenges are discussed. Furthermore, in this paper an IoT-Fog based framework for the prediction of crop disease is proposed. The proposed framework consists of four phases, sensor layer, fog layer, cloud layer and End-user. The proposed framework may be beneficial in the farming domain for the analysis of crop disease prediction in the early stage and may reduce the chances of disease outbreaks in the field

Farm monitoring and disease prediction by classification based on deep learning architectures in sustainable agriculture

Agriculture is necessary for all human activities to survive. Overpopulation and resource competitiveness are major challenges that threaten the planet's food security. Smart farming as well as precision agriculture advancements provide critical tools for addressing agricultural sustainability concerns and addressing the ever-increasing complexity of difficulties in agricultural production systems. This research proposed novel technique in agricultural farm monitoring and crop disease prediction using deep learning architectures. Here the monitored data has been collected based on IoT module along with the historical data of cultivation farm image data. This data has been processed for removal of noise removal and image resizing. The features of processed data has been extracted using deep attention layer based convolutional learning (DAL_CL) in which the features of data has been extracted. This extracted data has been classified using recursive architecture based on neural networks (RNN). The suggested system may use data categorization and deep learning to exploit obtained data and anticipate when a plant will (or will not) get a disease with a high degree of precision, with ultimate goal of making agriculture more sustainable.Experimental results shows the accuracy of 96%, precision of 89%, specificity of 89%, F-1 score of 75% and AUC of 66%.

Impact of temperature condition in crop disease analyzing using machine learning algorithm

In this study K-Nearest Neighbor (KNN) and Max Voting methods, we compare the accuracy rate and RMSE of the prediction system by using temperature data to predict crop disease. The crop disease is determined by analyzing the Kaggle crop data set, which contains sample sizes of 500 (Group 1 = 250 and Group 2 = 250) based on the temperature conditions. To improve accuracy and reduce RMSE, K-Nearest Neighbor (KNN) and Max Vote methods are used to predict crop diseases based on temperature conditions. Max Voting produced 91% accurate results, while K-Nearest Neighbor (KNN) produced 88% accurate results. According to the Max-voting method, the root mean square error (RMSE) is 3.97, while the K-Nearest Neighbor (KNN) method has an RMSE of 4.34. These two measures are significantly different (P = 0.0356, 0.0421 respectively). For crop disease prediction based on temperature, the Max Voting method performs better than the K-nearest Neighbor (KNN) method.

Digital Agriculture System for Crop Prediction Based on Machine Learning

Agriculture is the number one source of livelihood for approximately 58 percent of India’s population and is the most crucial part of GDP. Indian farming is based totally on economic advantages from crop yields, but now day’s agricultural generation has failed to verified satisfactory crop choice techniques and to boom crop yield in all over India. So, lower in crop yield will increase the trouble in farmer’s monetary health situations. So, it will become the maximum trending hassle for our agricultural region to invent such noble technique to advocate super appropriate crop for a particular region. To reap high-quality appropriate crop desire for areas primarily based on parameters like soil conditions, rainfall and weather we have got applied gadget studying method. Secondary hassle is lack of understanding or absence of steering even as farming. Lack of guidance in Indian farmers may follow incorrect farming strategies or inefficient traditional strategies. Most of farmers are uneducated and non-technical backgrounds so they'll be relying on conventional crop choice and farming techniques which falls them into reasonable loss. With the assist of disorder assessment tool, we predict the crop disease prediction and propose the precaution from the ones illnesses. Last and most essential hassle isn't any right marketplace assessment at the equal time as cultivation of any unique crop, which can also reason a cheap lack of farmers.