Description:Real-Time Crop Health Monitoring System Based on Artificial Intelligence and Machine Learning for Disease Detection

2. PROBLEM STATEMENT:
Food security is greatly contributed for by the agricultural act, modern farming though comes with many challenges especially in disease control. With crop diseases becoming more rampant in the recent past, yields and quality of the crops produced are at a high risk of being affected. Diagnostics of diseases in crops in the past have been done manually and this is tiresome, repetitive and contains a lot of individual errors. These methods usually include farmers or other agricultural professionals observing the plants and making a diagnosis of the disease and or infestation and then treating the former on the basis of the latter. This is very selective and time-consuming and can lead to delayed diagnosis and wrong diagnosis, and over application and or wrong application of the pesticides thus degrading the environment and the crops.

Also, due to the large size of the farms and the number of factors that comes into play such as weather risks, soil type, pests among others, it becomes virtually impossible for farmers to physically assess the status of the vegetation across large farming fields. Therefore, there is a pressing need to develop new, efficient, accurate and automated methods for diseases diagnostic that can help the farmer to reach a decision on time.

Old disease diagnosis techniques also do not capture real time data from other another vital aspect such as; weather conditions, moisture content of the soil, and the health of the plane in general. This leads to gaps in contacts to be made with the affected individuals and arrange for respective interventions to mitigate disease transmission or lessen their effect.

Current Challenges in Crop Disease Detection:
 Manual Monitoring: In the current disease monitoring methods, the diseases are usually checked through visual examination, which is very inconclusive and time-consuming especially for large scale farmers.
 Late Diagnosis: Often when people observe signs of diseases in the human body, by the time one has assessed this, the problem is usually deep-rooted making it difficult to check.
 Restricted availability: There is provision of an early diagnosis and a quick response to diseases that threatens the production of crops especially in the remote areas which mostly lack expertise in agriculture.
 Similar symptoms of crop diseases as pest or diseases affecting the crops or as stress or nutrient deficiency are some of the drawbacks of identifying diseases.
 Remaining vulnerability: this is brought about by the use of wrong diagnoses to recommend the use of pesticides and fertilizers which result in soil erosion, environmental pollution and the development of pesticide resistant diseases.
 Data Discreteness: This is characteristic by the failure to adopt relevant data in diagnostic imaging, climatic condition and soil qualities to enhance on predictive disease interventions.

In this regard there is a challenge of coming up with a real time accurate and reliable diagnostic technique for detecting crop diseases. If an Infectious Disease Identification System base on Artificial Intelligence (AI) and Machine Learning (ML), it can analyze this kind of big data from various kind of data source such as satellite images and drones, climate information, ground sensors and analyze disease symptoms, identify specific pathogens, and predict outbreak. Implementing such a system would help reduce severely the uses of human inspection or rather allow the farmer to take the necessary measures before something wrong happens. In addition, it would help in decision making to recommend an effective intervention that is timely, relevant, and cost efficient.

With the help of AI and ML, this system has the following capabilities:
 Approach the detection process in a sophisticated manner by increasing its efficiency.
 In many instances, identify the presence of a disease in its preliminary form when the sign of its existence cannot easily be observed.
 The use of multiple sources of data will lead to more accurate and reliable outputs in terms of predictions.
 It can send alerts to the farmers as and when the situation occurs so that they can act at the right time.
 Farmers and consumers should employ sustainable farming practices to reduce the use of pesticides and decrease a negative effect on the environment.
 The idea of the gadget meets the need of identifying crop diseases in a practical setting and can be integrated with other relevant innovations, making this invention feasible in different forms of farming.

3. EXISTING SOLUTIONS
There are also a number of methods that may be used in crop disease detection that starts from the traditional methods and up to the application of modern technology. But, these solutions also suffer from some drawbacks such as inaccurate results, scalability and sometimes it also consumes much time for the results. Here is the list of the major categories of the existing solutions:

1. Traditional Methods (Manual Inspection)
This has usually been done through visual assessment by the farmers themselves, without any expert’s intervention. This method involves use of the eyes to check for signs such as change in colour of the crops, appearance of wilting or lesion on the leaves and stems.
Limitations:
 Lack of efficiency: It is a time-consuming process to inspect large fields on farmland, and this will even be more time-consuming when it is a large-scale farming area.
 Subjectivity: The identification of diseases relates almost to experience of the farmer or the expert. This is due to the fact that it is not easy for most inexperienced personnel to be able to distinguish between similar signs that may be precipitated by different causes hence misdiagnoses often arise.
 Symptoms often only show up in the later stages, that means the spread of disease may have already reached its advanced stage and efforts to contain the diseases are going to be difficult.
 uneven: Disease diagnosis process is normally done by the observation of symptoms that could be hard to diagnose due to some factors such as light intensity, heat among other climate factors that affect the growth of plants.

2. Chemical and Biological Diagnostics
Most diagnostic methods fall under the chemical and biological diagnostic process that involves the testing of plant samples for pathogens. These tests can detect particular disease based on biochemical or molecular analysis which may include ELISA or PCR tests.
Limitations:
 Costly: These methods are also expensive since they rely on certain laboratory equipment and personnel.
 Poor Evaluation of Samples: The process of taking samples and sending them to a laboratory for testing might take some time, which postpones early diagnosis especially in large-scale farms that require quick judgements.
 High Costs and use of Laboratory: The aforementioned techniques are costly and involved laboratory which makes them unsuitable for large-scale, time-based disease surveillance in fields.
 Invasive: These methods involve taking samples from the plants and this might be deleterious to the crops since it might in some way affect their growth.

3. Remote Sensing and Satellite Imagery
Satellites, aerials and other remote sensing tools are employed for crop monitoring at some scale. They offer detailed pictures of crop fields that help in the detection of diseases including the early stage ones.
Limitations: The following are the limitations as the satellite imagery indicates
 Entire coverage: Large areas can be easily imaged at a time, but disease development, especially at an early stage requires higher resolution for imaging. They can be more accurate, but its coverage area is quite small and they are costly to mobilise.
 Big Data: Remote sensing provides big data that are difficult to process and analyze without the help of computers or other technology. Primarily, the identification of diseases from such images is a very tedious process and it also involves a lot of experience in image analysis.
 Environmental Interference: This is another factor that limits the use of satellite or drone images in detecting diseases, and it is usually caused by factors such as clouds, rain, or inadequate lighting.

4. AI-Based Disease Detection Systems
Several methods have been designed in this generation to enhance the detection of diseases with the use of image processing and patterns of machine learning. These systems employ the use of image processing for identifying signs of diseases in plant images.
Limitations:
• WithLack Of Access To Sizeable Datasets: The training of AI models can often be done by using a large number of labeled datasets. Among these, often it is difficult to find sufficient labeled data for rare diseases or for small geographical regions which affects its ability to perform well in new environments.
• If AI models are not exposed to other data sets then it tends to over-emphasize on these features and does not generalize well to other scenarios: Overfitting.
• Limited Integration of Multiple Modalities: Some current AI systems work only with the images attained from visual reconnaissance and do not take data from other sensors like moisture, temperature, and climate into consideration. This makes it difficult for them to give better information concerning the wellbeing of the crops grown in the farming areas.
• Standalone AI systems will have high computational requirements: Another issue that arises from the architecture of today’s intelligent systems is that the methods used to train modern intelligent models and to make predictions or decisions involve computations that could be time-consuming and require significant amount of computational power, especially in some of the state of the art deep learning methods. This can be a big issue that contributes to low adaptation in resource constraint environment.

5. Mobile Applications for Disease Identification
Mobile applications have been created such that farmers can photograph crops and get a diagnosis concerning the images. Some apps also contain features that allow comparing the user’s images with some database of possible diseases.
Limitations:
• Accuracy Factor: Most mobile applications use fundamental image recognition techniques that might not assist in detecting diseases due to some factors like stage of growth or changes in climate.
• Lack of User Control: The tool is dependent on the user input and sometimes it may be difficult for the user to provide high quality images for the program to make a correct diagnosis off. Thus, image quality will be a deciding factor for getting the needed results.
• Lack of Interaction with Other Farm Data: These applications are generally developed for the purpose of disease identification, without considering other parameters such as the soil, weather conditions, or plant growth progress.

Still, all these current solutions provide the following challenges: detection delay, high implementation cost, the problem of scalability, and a lack of integration with multiple data sources. Existing approaches for disease monitoring either cannot process real-time data of large scale or they don’t offer broad and complete automated analysis required to promote effective crop management. This calls for a more innovative, comprehensive and scalable model which utilizes AI and ML in timely disease diagnosis which will involve data source such as imagery, sensors, and weather among others to produce timely and accurate information to the farmers. This is where the Real-Time Crop Health Monitoring System comes into play this paper which helps in filling the mentioned gaps by offering enhanced automated and datasheet solution to the disease detection in crops.
Preamble
The current invention concern a Real-Time Crop Health Monitoring System in which both AI and ML are used in order to diagnose the health of crop in order to detect diseases of cropped plants as well as classify them. This system comprises several aspects such as high-resolution imaging including drones and satellite images, environmental condition like soil moisture and temperature, and other clinical data such as weather conditions to come up with a one-stop solution towards crop monitoring. Through the use of CNN for image analysis of healthcare quality images and ensemble learning technique in structured clinical data, the system makes it possible to diagnose diseases before they manifest. Also, transfer learning is employed in the system to increase effectiveness, especially when there is a shortage of training data. The monitoring system is validated using cross-validation techniques such that the accuracy of the disease diagnosis is high. In addition, the invention applies explainability tools such as SHAP and LIME to enhance the dominance and trust in the model for prediction. This they said, will help to advance the objective of enhancing food security and specifically target crop diseases to reduce pesticide use and increase crops yields through a reliable data and enhanced automation.

6. Methodology
Real time Crop Health Monitoring System has a logical framework consisting of several technological subcomponents that enable the assessment of diseases early enough. The following is a detailed procedure followed in the procedures of developing and implementing the system.

1. Data Collection and Preprocessing
Data Sources:
• Imaging Data: Within crop, images data with higher resolution involve use of drones, satellite, and field camera. Such images include symptoms of the diseases on the plant body indicating discoloration, lesions and wilting.
• Clinical Data: Although the firm collecting data from farm sensors, weather stations, and clinical records in the form of soil moisture, temperature and pH among others.
• Medical data: Disease development data, climate data like temperature, and relative humidity and precipitation rates are incorporated to assist in disease development.

Preprocessing:

• Based on these works, several image preprocessing steps are undertaken as follows; images are normalized, images are resized and additional operations are performed such as flipping, rotation, and scaling to enhance the images’ quality and to ensure that the model is not easily tricked by any form of image distortion.
• Data Cleaning: Missing value in structure data are treated by imputation or delete columns based on some threshold value when it come to outlier.
• Feature Learning: Autoencoders are employed for dimensionality reduction of clinical data to a point that it can be used for more analysis.

2. Model Development
Convolutional Neural Networks (CNNs):
• CNNs are employed to process medical images since they are considered to be of a medical standard. These are supposed to learn features directly from raw images for the identification of diseases symptoms such as lesion, spots, and other anomalous growths. Here the CNN consists of more than one convolutional layers, pooling layers and density layers and two class activation by a softmax transform.

Ensemble Machine Learning Models:
• Most algorithms including Random Forest and XG Boost are developed and used on tabular form data such as soil properties and weather conditions. These models are specifically programmed in order to predict the presence of diseases using database information and related environmental factors.
• Multi-Input Models: Image-based and clinical data are combined using CNNs and fully connected neural networks respectively to develop multi-input models that analyze both types of data.

Transfer Learning:
• ResNet and EfficientNet models among many pre-trained CNN models are fine tuned on crop disease datasets. Such a transfer learning means that the model can rely on the knowledge obtained from the large and common datasets without the necessity of training on large and specific crop disease datasets.

3. Multi-Modal Data Fusion
Fusion Techniques:
In other occasions, both the clinical and imaging data are combined using data fusion in order to provide a better prognosis by the system. As with the previously described techniques, the fusion can be made at different stage:
• Preprocessing: Feeding raw data provided either as pixel values of provided image(s) and/or patient’s clinical information to the neural network without prior processing.
• Late Fusion: Each individual model deals with different type of data ( e.g., CNN for images, RandomForest for clinical data) and its results are fused at the decision level.
• The model combines both the early and late fusion techniques so that it can extract the features from both the imaging and the structured data modality.

4. Model Evaluation
• Model Performance: This is done to check the model’s capability of working for different subgroups of the data using K-fold cross-validation.
• Performance Metrics: Added to this, the three-common evaluation measures of a classifier, namely, accuracy, sensitivity and specificity test whether the model is efficient in distinguishing diseased crops from healthy crops.
• AUC-ROC, F1-Score: These two measures are mainly used in case the datasets are unbalanced.
• Calibration Curves: It is a type of probability scale which is used to check the rate of accuracy of the model in classifying the existing patterns of data.

5. Explainability and Trust
• SHAP (SHapley Additive exPlanations): Centers are used to explain the outcomes of the machine learning model which gives information about the feature importance in the prediction like the impact of each environmental factors that plays a role in disease discrimination.
• LIME (Local Interpretable Model-agnostic Explanations): When performing local decision analysis, LIME helps explain the results obtained on a particular data instance, which is important to have the support of farmers and various other stakeholders.
• Grad-CAM (Gradient-weighted Class Activation Mapping): Grad-CAM is applied to the CNN model to enable farmers have an understanding of where specifically the model is focusing with relation to the crop images used for disease detection.

6. Real-Time Monitoring and Alerts
• Real-time Detection and Surveillance: The mobile application which utilizes the deployed model on its local end on the edge devices is aimed at having real-time disease detection on local farms, on mobile devices, or even drones for surveillance and management.
• Alert System: Whenever an ailment is identified, there is immediate notification to the farmer through an app, or any other preferred method of the farmer. Some of the alerts include; the type of disease that has been detected and further recommended action should be taken.

Figure 1. Methodology Proposed
The developed methodology utilises the best approaches in artificial intelligence and machine learning to form a practical real time crop monitoring and diseases diagnosing system. Since the existence of the system implies that data is gathered from a number of sources and analyzed with the help of machine learning algorithms, it guarantees high accuracy and reliability of the results, which enable farmers to take active decisions on crop diseases prevention and treatment.

7. Result
The format used in the results section is focused on presenting the obtained outcomes of the Real-Time Crop Health Monitoring System along with the performance of the models used, the evaluation of the results, and conclusions from their visualization. This section also proves the validity of the system that is developed in this research for the identification of crop diseases and presents the advantages for farmers and those associated with agricultural activities.

1. Model Performance Evaluation
Therefore, as part of the testing process, a number of datasets associated with crops diseases were used to fine-tune the performed models by early detection of the diseases and multi-class classification of the diseases. The model was further validated by applying k-fold cross validation so that it would determine the similarity of the specific set of scores.

The performance indicators applied in this work to draw a comparison between the models are:
• Accuracy: It is the ratio of the correctly classified instances to the total number of instances.
• Recall, Sensitivity: Cent percent of positive instances of diseased crops have been predicted correctly by the model.
• Specificity: High performance of the model in the classification of healthy crops.
• AUC-ROC: A measure of the ability of the model of this study in differentiating diseased crops from the healthy ones, calculated using the receiver operating characteristic curve.
• F1 Score: The average of the precision and the recall rate used in case of a balance between the false positive and false negative results.

Table 1: Performance Metrics Table
Model Accuracy Sensitivity Specificity AUC-ROC F1 Score
CNN for Image Data 92.50% 94.30% 90.00% 0.96 0.93
Random Forest (Clinical Data) 89.40% 87.60% 91.20% 0.92 0.89
XGBoost (Clinical Data) 90.20% 89.00% 91.80% 0.93 0.9
Hybrid Model (CNN + Random Forest) 94.70% 96.00% 93.40% 0.98 0.95

Figure 2. performance metrics generated and compared

2. Confusion Matrix for Hybrid Model:
The following confusion matrix presents the results of the proposed hybrid model that combine CNN with Random Forest in distinguishing diseased and healthy crops. The diagonal of the matrix indicates the proportion of correct classifications for each group which helps to understand the performance of the model to distinguish between classes.

Table 2. generated Confusion Matrix
Actual \ Predicted Healthy (0) Diseased (1)
Healthy (0) 3800 120
Diseased (1) 90 3200

Figure 3. Confusion matrix generate

The ROC of the hybrid model is presented below. The curve plots the True Positive Rate (Sensitivity) against the False Positive Rate (1 - Specificity). A higher numerical value of AUC-ROC refers to better performance of the model as it detects the diseased crop from the healthy crop.

Figure 4. ROC curve of the hybrid model
The calibration curve compares the probabilities of disease presence as predicted by the tool with observed probabilities. A model that has high calibration means that the predicted probability values are nearly equal with the actual probabilities of the events.

Figure 5. Calibration curve of the hybrid model

In real-time of disease diagnosis, the observed probability shown in table 5 accompanied the probability produced by the hybrid model, which confirms that it is well calibrated.

3. Explainability of Model Decisions (SHAP Values):
To increase the credibility of the results and present the model’s vision in a clear and comprehensible manner, we used SHAP (SHapley Additive exPlanation) for explaining feature importance.

Figure 6. SHAP feature importance plot for hybrid model

This is evident from the plot that in addition to the image features extracted by the CNN, temperature and soil moisture are other environmental factors that contribute in the detection of disease.

4. Real-Time Disease Detection and Alerts
The knowledge transfer was triggered with the real time disease identification and notification of the farmers when the system was deployed. It may analyze images and sensor data on edge devices so that disease outbreaks are easily detected and subsequent actions are immediate. These included disease type and the recommended treatments which were delivered to the farmers by use of mobile applications.
• Alert System: Farmers were given alerts automatically, with disease discolor status and information on the basis of the model generated.
• Real-World Application: During pilot field test, instances with signs of early-stage fungal infection in crops was identified and: evidenced with follow-up laboratory test this proves that this system could urge early intervention.

The findings of the Real-Time Crop Health Monitoring System present indicate on the accuracy and effectiveness the crop disease diagnosis in real-time monitoring. This versatile system is capable of early disease detection, proper diagnostic of the diseases, and monitoring process due to the utilization of AI and ML techniques as well as multi-modal data fusion. SHAP and LIME make the model transparent, which can help in the agricultural industry to apply sustainable and accurate farming. The components of real-time alerting and disease classification provide substantial value to farmers such as increasing crop yields, decreasing the amount of pesticides used, and optimizing farm management.

8. Discussion
The Real-Time Crop Health Monitoring System described in this paper which uses AI and ML for disease detection has the potential to address most of the problems faced by modern agriculture regarding disease control. Program emphasizes on the efficiency of the system, efficiency differences between the method and the conventional methods, and possible impacts on farmers and the agricultural sector.

Model Performance and Evaluation
Outcome: The evaluation of the proposed Model performance show that the Hybrid Model (CNN + Random Forest) is the most effective Model according to accuracy 94.7%, sensitivity 96.0%, specificity 93.4%, AUC-ROC 0.98 and F1 score 0.95. These findings also show the effectiveness of the system when it is made up of both the image processing data and the clinical data which includes the Soils environment. The result proved the effectiveness of hybrid model where CNN is integrated with high accurate image processing while Random forest is a structured data processing model.

The performance of individual models also demonstrates the effectiveness of the proposed strategy of using the hybrid model. When it was applied for the identification of the diseases through its CNN model deployed for image analysis of drones and satellite images its accuracy was 92.5% which is quite impressive with regards to the fact that even patch patterns cannot be easily detected by the naked eye. Random forest and XGboost models aimed at clinical predictors targeted an acceptable accuracy as well, with the rates of 70.8% and 72.8% accordingly. These include that the imaging data is essential for diseases diagnosis, however, clinical data can improve the accuracy of the diagnosis if other data is integrated into the imaging data.

Cross-Validation and Generalization
K-fold cross-validation helps in making certain that the models developed are stable and can fairly generalize over the partitions of data. Nonetheless, the high accuracy obtained by all ten models repeatedly on every fold demonstrates that data overfitting has not occurred and the system can work on many different datasets. This is an important aspect when it comes to application since it makes it possible to train the model under various conditions to be effective for farming types.

Explainability and Trust
One point why the Real-Time Crop Health Monitoring System is beneficial is because of its explainability, a factor that is essential when it comes to credibility among the intended users – farmers. SHAP and LIME are used to interpret how each feature is being used in the models to make predictions. This is a crucial point in the decision-making process, especially in the agricultural system where farmers need to have the confidence in the system through the kind of decision made to them. The farmers believe the system since the environment will be explained to them for instance if soil moisture or temperature is critical for disease determination.

Practical Implications for Farmers
Among the advantages that are associated with this type of a system are the fact that it is effective in diagnosing diseases at an initial stage, even before the disease manifests itself in the body. They can easily advise farmers and implement control measures before the diseases prevail and the yields have reduced. For instance, infestations can be treated at an early stage, by the use of anti-fungal means, hence low use of pesticides. This not only proves economic advantage for the farmer to cut on the costs of pesticide but also it is the right way for environmental conservation.

The aspect of real-time investigation and disease alarm system ensure farmers are aware of the conditions ailing their crops and how to contain them. In large farms, the use of this feature can be very helpful since doing them manually will not be efficient. The fact that the system can work locally was enhancing through edge devices such as a farmer’s mobile phone or local farm server means that farmers are able to access disease prediction offline.

Comparison with Traditional Methods
Conventional methods of detection of crop diseases involve field inspection which is gradual, tiresome and sometimes includes errors as wells as is a time consuming method. The AI-based system is adaptive, less erroneous, and has the ability than the traditional method to control large portions of land. With an instance, it is capable of solving problems and interpreting data from a number of sources like images, sensors, even weather conditions than it would be feasible for a human expert to manually inspect. This is have possibly been of great help since the farmers can not be able to afford to employ professional help and some of them from distant areas.

Even though conventional methods are effective in some cases, the option developed by Barclay Crop Health Monitoring System LLC is a far more effective solution which use modern technologies putted into practical use that helps farmers make effective decisions in real time.

Challenges and Future Directions
Nevertheless, several challenges must be discussed to consider in order to achieve a wider use of the system:

• Data concerns: The sound performance of the system highly depends on the quality of the input data obtained. However, in several areas of the world particularly the developing nations, obtaining high resolution images or dealing with broader clinical information may be a challenge. There is a possibility that to overcome this challenge might need to engage agricultural organizations, government or technological companies for the availability of right data.
• Model Stability: Certain aspects are already well implemented in the current models such as the crop species, the environment, and the disease types of the used dataset, therefore the system should be able to handle other crop species, other environmental conditions and other types of diseases. They should use these models to continue the research and authenticate them to suit different agravarian environments and environments in specific regions.
• The integration with other utilization tools is necessary for the fulfillment of the development objectives of this system, where farm management software should include crop health in other forms of data used in farming, such as irrigation schedules or fertilizer tracking. This could be the reason for moving towards a more overall farm management and sources of investment.

Therefore, the proposed Real-Time Crop Health Monitoring System has successfully illustrated that crop diseases can effectively be detected by using AI and ML techniques. Due to the multi-source data acquisition, high accuracy, and ability to give real-time alerts, the system can offer considerable advantages for modern farming, including early car of diseases, cost effective, and effective resources management. The application of explainability techniques features the system to be credible especially to the farmers so that to be in a position to make data-driveness decisions for a proper crop management. With subsequent developments of this system being adopted to farming, the crop disease management all over the globe could change leading to improved and healthier crops as well as increased yields than in the current climate smart farming.

9. Conclusion
Real-time Crop Health Monitoring System, with integration of AI and ML have been described as one of the most innovative steps for identifying disease in crops. Thus, by using the integration of multiple sources like image data, environmental data, and data from the clinics, the system offers a real-time multi-source data system to diagnose the disease of crops at an early stage. The performance of the proposed system was tested on different crops and the results showed high accuracy and reliability of the system having AUC-ROC of 0.98 sensitivity of 96.0% and accuracy of 94.7%.

It is evident that the proposed approach that integrates CNNs for the image processing of the disease and ensemble machine learning models for the clinical records yields high accuracy for early disease diagnosis. This approach is far much efficient and accurate as compared to the traditional approach to inspection that involves manual inspections which is time-consuming, repetitive and often involves human errors.

Another of the proposed system’s features is that the rationales for arriving at a particular decision by the model are made clear and comprehensible to the farmer. SHAP and LIME give farmers a level of faith in the model by explaining every prediction made, thus, the use of models in farming is highly recommended.

Additionally, the implementation of the disease identification and notification in real-time assists the farmers in preventing diseases from devastating crops, increasing yield, and decreasing the use of pesticides, which is the key to sustainable agriculture. As such, the system also enhances the idea of precision agriculture- the change from prominent traditional manual farming to more efficient and sustainable farming based on information.

Despite these limitations that are associated with data acquisition, flexibility of the models across the crop types and compatibility with existing farming management systems, the future of this system in the detection of diseases in agriculture is very promising. It is possible to enumerate future developments where traits such as the probability of its future development, the potential value of expansion, the benefits that the new features could bring to the farmer and the value of additional crops and regions to the growers.

Therefore, Real-Time Crop Health Monitoring System is a modern didactic tool for farmers which help to enhance the crop health, fight against diseases and pursue sustainability in agricultural practices. It is on the premise of these challenges that the system is set to provide food security and practice and enhance environmentally and sustainable agriculture.
, Claims:Claims
1. A real-time crop health monitoring system for the detection and classification of crop diseases is as described below.
• A collecting system to gather both the image data such as, aerial images, satellite images, environmental data such as moisture content, temperature, clinical data such as weather conditions, pH and many others.
• A data preparation module for cleaning the gathered data, enriching the data and also for decreasing the features of the data.
• A disease detection model involving CNN for image data analysis and applying multiple machine learning algorithms for the clinical data.
2. The system of claim 1, where the disease detection model is a CNN and RF/XGBoost for the case of crops based on medical-grade images and standard clinical data to be structured for disease recognition.
3. The system of the claim 1 is further including the multi-modal data fusion module that combines clinical and imaging data using one of the following fusion types:
• The Early Fusion strategy entails using raw image and clinical data simultaneously before passing through the model.
• Late Fusion: The amalgamation of the general outputs of several models built separately based on clinical and imaging information.
• Hybrid Fusion: Combining early and late fusion techniques for enhanced disease detection.
4. The system of claim 1 where the disease detection model applies transfer learning to improve performance, where ResNet or EfficientNet pre-trained model are applied on the crop disease dataset to gain better performance and avoid over-training.
5. The disease detection model, which enables the system of the embodiment of the present invention, is cross-validation and evaluated in terms of accuracy, sensitivity, specificity, AUC-ROC and F1 score.
6. The system of claim 1, where the dissemination of disease information to the farmers is also included in real-time alerting system to notify the disease classification, the degree of disease, and the potential management measures based on the recognition system of the model.
7. The system of claim 1 is also equipped with explainability tools namely SHAP, SHapley Additive exPlanations and LIME that helps in providing the transparency and interpretability of the disease detection model.
8. The system of claim 1 wherein the Disease Detection Model is run on smart phones, local farm servers etc to allow constant disease detection and tracking, which is time effective and independent of internet connection.
9. The disease detection system according to the present invention is to cooperate with the currently conventional farm management systems to provide the farmers with crop health records in conjunction with other farming chores like irrigation and fertilization.
10. This invention is a procedure for identifying crop disease and categorizing them in real-time, and its steps include
• Obtaining data from multiple sources which may be imaging data, environmental data, clinical data or any other data.
• Using this we pre process the data to transform it into a more suitable format and more manageable set of features.
• That is, we will propose the strategy based on the convolutional neural networks for image analysis and the ensemble of other models for clinical data analysis prognosis of diseases.
• Creating real time alarm and suggestions on the disease type for farmers.
• Interpretability and explainability of decision-making through and for the model can be helped by such tools as SHAP and LIME.