NUTRIENT DETECTION DEVICE

Abstract

The present invention relates to an AI-based manure nutrient detection device designed to analyze fertilizer content in real-time. The device comprises enclosure that supports the collection manure consistencies through sampling slots. It features a multi-spectrum sensor module comprising infrared (IR), near-infrared (NIR), and chemical sensors to measure nitrogen (N), phosphorus (P), potassium (K), pH, moisture, and temperature. The AI processing unit applies machine learning algorithms such to provide reliable nutrient assessment and fertilizer recommendations. It has a interface with crop-specific application guidance. Using Bluetooth and Wi-Fi connectivity, it supports cloud storage and is compatible with farm management systems. With 5000 mAh battery, it guarantees portability, and accurate automation calibration and quality control mechanisms ensure the performance of the product. Weather-resistant casing ensures the usability of the product in the field with reliability and optimal fertilizer use support to sustainable farming practices.

Specification

Description:This AI-Based Manure Nutrition for Fertilizer Detecting Device 100 is developed to enhance agricultural practice through real-time nutrient content assessments that promise better crop yield as well as environmental sustainability. It is multicomponent for delivering precise fertilizer recommendations as one of the means of enhancing crop outcomes while at the same time considering environmental safety. This device is developed with user convenience, accuracy, and continued learning in mind, ensuring that function is correctly under any conditions of field.

The enclosure (101) includes a sample input slot for samples of manure having different consistencies, that is, solid, semi-solid, and liquid. The multi-spectrum sensor module (102) is another important element is placed inside the enclosure 101, and it uses several sensors to collect data extensively about nutrients. Among such sensors include the following ones:

Infrared (IR) sensor (102A): It functions in the range of wavelength between 2.5-25 µm at a resolution of 4 cm?¹; this sensor identifies compounds with the presence of organic material as well as moisture.
Near-Infrared (NIR) sensor (102B): It measures between 900-1700 nm with a resolution of 10 nm, measuring important chemical bonds that are related to nutrients
Chemical sensors 102C: Selective ionic electrodes for determining the levels of N, P, and K with 1 ppm sensitivity. Other sensors include a pH, moisture and temperature measurement that keeps the analysis as accurate as possible regardless of the environment.

AI-Powered Analysis and Recommendations:-
An AI processing unit 103 analyzes sensor data using machine learning algorithms. The proposed model for the AI processing unit 103 integrates a combination of Convolutional Neural Networks in interpreting spectral data and makes use of Random Forest models in feature selection with outcomes from Gradient Boosting Machines leading to accurate predictions. The reinforcement learning algorithm enables fertilizer recommendations to be improved with continuous input through new data acquired over time, thus making fertilizer recommendations ready for the changing environmental factors, soil types, and manure properties. The AI processing unit 103 is pre-trained with crop-specific fertilizer requirement datasets so that it generates actionable recommendations for different crops and types of soils.

User Interface 104 and Connectivity:-
The touchscreen display in user interface (104) depicts real-time graphical feedback on nutrient levels, moisture, pH, and temperature. It is color-coded indicators of the nutrient status, graphical balance charts and crop-specific fertilizer recommendation are customized according to the user needs. In this interface, there are also historical comparisons wherein the farmers directly input specific environmental parameters and types of crops in order to get nearer to the correct recommendation.

The module also has the ability to connect through both Bluetooth and Wi-Fi to smartphones, tablets, and cloud-based storage systems, that makes data transfer very easy and natural. This helps farm management software monitor long-term soil health and manure quality.

The module also has integrated features that ensure data security through end-to-end encryption by a secure cloud storage and authentication for access by the user so data handling is safe and efficient.

Power and Calibration Features:
A rechargeable Lithium-ion battery 106 of 5000 mAh ensures continuous operation for up to 10 hours. USB-C fast charging support allows the recharging of the battery within two hours, making it highly suitable for field use. An automated calibration unit 107 with internal reference standards maintains sensor accuracy over time. This device is self-calibrating weekly and is calibrated manually every month using special solutions. In addition, there is an annual professional service available for this product to ensure the highest performance.

Error Handling, Quality Control, and Field Durability:
The device includes features that are enabling to detect sample contamination, insufficient sample volume, faulty sensors, reliability and error handling and quality control mechanisms 108. Measurement of each nutrient is supplied with a corresponding confidence interval and the system is designed to raise an alert if calculated values exceed or fall below the predicted ranges, which calls for re-assessment if deemed necessary. The weather-resistant casing (109) and makes the device rugged enough to withstand aggressive field conditions. The physical design is rather light (1 kg) and compact (25 cm x 15 cm x 8 cm) and serves to make the device portable and easy to handle in agricultural fields.
There are several error handling and quality control included in the device:
1. Automatic detection of sample contamination or insufficient sample size
2. Real-time diagnostic sensor measurements to identify malfunctioning components
3. Measured nutrient values with accompanying confidence intervals
4. Out of range measurement with audible alarm and flashing lights asking the user to check

Regulatory compliance and environmental considerations:
The equipment is manufactured to meet the international agricultural and environmental requirements and is consistent with USDA and EPA nutrient management standards, EU Nitrates Directive and ISO 17025 calibration standard. All parts used for this device is RoHs compliant and use lead-free solder in its assembly. High precision moulded sensor housings provide the best possible alignment and have minimal chance of human error during assembly.

End-to-End Process for Manure Analysis and Fertilizer Recommendation:
It works on a systematic cycle wherein accuracy of the nutrient analysis and fertilizer recommendation provided in the output is guaranteed:
Input Sample: Collect the manure sample and insert it into the input slot
Multi-Spectrum Analysis: IR 102A, NIR 102B, and chemical sensors 102C analyze all the types of nutrients.
AI-Based Data Processing: The AI processing unit 103 interprets the data and matches it with the internal nutrient database residing within.
Real-Time Feedback: The touchscreen displays nutrient levels, pH, and temperature with fertilizer recommendations.
Data Transfer and Storage: Results are transferred to mobile devices and synchronized with cloud storage for long term tracking.
Continuous Learning: The AI model continues learning over time with enhanced recommendations from newer data and environmental inputs.
Embodiments-
Enclosure 101 for device:
In this apparatus, there is a slot for inputting the sample manure, a module of multi-spectrum sensors 102, AI processing unit 103, a display screen 104A, and wireless connectivity module 105.
Multi-spectrum sensor module 102:

Multi-spectrum sensors module 102: It comprises IR sensor 102A, NIR sensor 102B, and Chemical sensors 102C. This sensor perceives and measures the concentration level of the following:
Nitrogen (N)
Phosphorus (P)
Potassium (K)
Organic Matter Content
These nutrients are necessary in order to calculate the manure fertilizing value. The multi-spectrum sensor module 102 collects the information of the manure sample and forwards it to the AI processing unit 103.
Sensor specifications:
These include the following:
IR Sensor 102A (wavelength range 2.5-25 µm, resolution 4 cm^-1)
NIR Sensor 102B (wavelength range 900-1700 nm, resolution 10 nm)
Chemical Sensors 102C (ion-selective electrodes for N, P, K and detection limits of 1 ppm)
Accuracy: ±2% for main nutrients (N, P, K), ±5% for organic matter
Detection range: 0-5000 ppm for N, P, K; 0-100% for organic matter

AI processing unit 103:
The AI processing Unit 103 is pre-trained programm on a dataset of different manure samples. The AI, through machine learning algorithms process sensor data compared against predefined optimal nutrient ranges for various crops. The AI learns from new manure samples for updating its model to improve the accuracy of its predictions.
- The AI processing unit 103 follows the data given by sensors, learning with each of them and evolving over manure types as well as environmental factors. The AI system is developed to learn and get better over time that, as a result, provides even better results in terms of the determination of nutrient levels.
The AI processing unit 103 is capable for accounting of
- Environmental conditions (temperature, moisture)
- Manure types (solid, semi-solid, liquid)
- Crop types and specific needs of crops
Specific AI Algorithms-
1.Convolutional Neural Network (CNN) for spectral data analysis
2.Random Forest for feature importance and as a preliminary prediction.
3.Gradient Boosting Machine (GBM) for final predictions about the nutrient levels.
4. Reinforcement Learning algorithm for continuous model improvement:
- The AI model is pre-trained on a dataset of more than 100,000 manure samples coming from diverse sources and regions.

User Interface 104 and Display 104A:
It employs a user-friendly, intuitive touchscreen User Interface (104) is used by the less-technologically-inclined user, such as small and medium-scale farmers. The interface displays the levels of nutrients in an understandable form as well as providing clear instruction on adjusting fertilizer application.
- The touch-screen interface displays real-time results, which includes a breakdown of the nutrient along with fertilizer recommendations.
- The interface provides the following features:
1. Level indicators of Nutrients are color-coded
2. Graphical representation of nutrient balance
3. Crop-specific recommendation output
4. Historical data temporal trend and comparison
How It Works:
1. Feed the manure into the input slot
2. Sensors are used to sense the nutrient content and chemical content.
3. AI Feeds the analyzed data back to real time

Portable Handheld Design:
The invention is designed as a handheld, lightweight and weighing approx 500g and measuring 20cm x 10cm x 5cm, that allows the device in the field. A manure sample is easily put into the device, and results immediately available to farmers so the farmers do not need to take manure to a lab. Compact design of this apparatus promises the possibility that farmers of any scale is able to take it and make it work easily.
Real-time results and fertilizer recommendations:
The AI analyses the data that gives real-time results on the nutrient composition. Such results are then represented on the touchscreen user interface 104 that provides a breakdown of contents such as nitrogen, phosphorus, and potassium. If the nutrient levels of the manure are outside the optimal range, the device comes up with recommendations for readjusting fertilizer contents by adding nutrients in specific composition is able to balance the manure content.

Data Connectivity and Storage:
The device is equipped with a wireless connectivity module 105 for connectivity like Bluetooth and Wi-Fi, thus allowing for farmers to transfer data to a smartphone and cloud storage system for long-term tracking.
It allows the user to:
-Keep nutrient trends in manure over time
-Make fertilizer application decisions based on data.
-Share analysis results with agronomists or consultants.

Data Security Measures:
1. Encryption of data transfer end-to-end.
2. Cloud storage that is secure and anonymous.
3. User authentication to access the stored data.
4. Regular security audits and updates.

AI Adaptability:
The adaptability of the AI processing unit 103 continues to learn and adapt based on new data and feedback inputs, including changes of manure composition and environmental factors. This process of continuous learning ensures that it is relevant and applicable to any agricultural context, providing accurate recommendations regardless of crop or region.

Power Source and Battery:
The device is powered by a rechargeable lithium-ion battery (106), so there is no need for a separate source of power, thus making it ideal for use in the field. The battery life has optimized it for long term use during the day, which makes it convenient for many farmers working at remote locations. Highly informative on specific details of the batteries:
-Capacity in mAh-5000
-Operating time: Continuous 10 hours
-Charging time: 2 hours for full charge
-Supports USB-C fast charge.

Additional Embodiments:
An enhanced environmental and economic impact:-
The invention significantly reduces the threat of over-fertilization and under-fertilization by providing accurate nutrient analysis in real time.
Some of the major benefits of this include:
- Saves the environment as it does not soil the land and contaminate water sources due to excessive use of fertilizers.
- Gives cost-saving benefits to farmers from the removal of unnecessary costs associated with expensive lab tests and also minimizes fertilizer wastage.
- Promotes yields from crops through optimized application of nutrients for increased profitability in farming.

Calibration Process:
The system is a self-calibrating process that automates and ensures that accuracy is maintained.
1. Automatic weekly self-calibration using internal calibration standards
2. Calibration done by users monthly with reagents provided
3. Annual professional calibration is recommended for ultimate performance

Manufacturing Process:
The device is manufacturing from:
1. Heavy-duty, weather-resistant plastic casing.
2. Sensor housing is precision-molded for optimal positioning.
3. Lead-free solder on all electronic parts.
4. Assembly process under programmable control for consistency, reduce human error.

Regulatory Compliance
The device is manufactured to meet relevant agricultural and environmental regulations, including:
1. USDA and EPA guidelines on nutrient management
2. EU Directive on Nitrates
3. Laboratory for testing and calibration - compliant with the ISO 17025
4. Electronic components in place are RoHS compliant.
, Claims:1. An AI-Based Manure Nutrition for Fertilizer Detecting Device 100, comprising:
an enclosure 101 with a sample input slot configured to receive manure samples of various consistencies;
a multi-spectrum sensor module 102 integrated within the enclosure 101, comprising: an infrared (IR) sensor 102A with a wavelength range of 2.5-25 µm and a resolution of 4 cm^-1;
a near-infrared (NIR) sensor 102B with a wavelength range of 900-1700 nm and a resolution of 10 nm;
chemical sensors 102C including ion-selective electrodes for detecting nitrogen (N), phosphorus (P), and potassium (K) with detection limits of 1 ppm;
additional sensors for measuring pH, moisture content, and temperature of the manure sample;
an AI processing unit 103 operatively connected to the multi-spectrum sensor module 102, configured to:
a) Analyze data from the sensor module using a combination of machine learning algorithms including Convolutional Neural Networks, Random Forests, and Gradient Boosting Machines;
b) Compare detected nutrient levels to a database of optimal nutrient ranges for various crops and soil types;
c) Generate real-time recommendations for fertilizer application based on the analysis;
d) Continuously learn and adapt its models based on new data inputs and environmental factors; a user interface 104 comprising a touchscreen display, configured to:
- present nutrient levels, pH, moisture content, and temperature data in a graphical format;
- display 104A fertilizer recommendations and application instructions;
- allow user input for specifying crop types and environmental conditions;
a wireless connectivity module 105 supporting both Bluetooth and Wi-Fi protocols, enabling:
i) Data transfer to external devices such as smartphones and tablets;
ii) Cloud synchronization for long-term data storage and analysis;
iii) Integration with farm management software and decision support systems;
a rechargeable lithium-ion battery 106 with a capacity of at least 5000 mAh, providing up to 10 hours of continuous operation with 2 hour of full charging time and USB C support for fast charging;
an automated calibration unit 107 with internal reference standards for maintaining accuracy over time;
error handling and quality control mechanisms 108 to detect sample contamination, insufficient sample size, and sensor malfunctions; and
a weather-resistant casing 109 designed for field use, with the entire device weighing no more than 1 kg and having dimensions not exceeding 25cm x 15cm x 8cm.

2. The device as claimed in claim 1, wherein the AI processing unit 103 continuously learns from new manure samples and adapts to different environmental conditions and manure types, providing tailored recommendations based on real-time analysis.

3. The device as claimed in claim 1, wherein the multi-spectrum sensor module 102 comprises infrared 102A, near-infrared 102B, and chemical sensors 102C that work together to deliver a comprehensive nutrient analysis of the manure sample, with specific sensor specifications including:
- IR Sensor: Wavelength range 2.5-25 µm, resolution 4 cm^-1
- NIR Sensor: Wavelength range 900-1700 nm, resolution 10 nm
- Chemical Sensors: Ion-selective electrodes for N, P, K with detection limits of 1 ppm

4. The device as claimed in claim 1, wherein the AI processing unit 103 is pre-trained on a large dataset of manure samples and fertilizer requirements for different crops and soil types, and uses a combination of machine learning algorithms including:
- Convolutional Neural Network (CNN) for spectral data analysis
- Random Forest for feature importance and initial predictions
- Gradient Boosting Machine (GBM) for final nutrient level predictions
- Reinforcement learning algorithm for continuous model improvement

5. The device as claimed in claim 1, wherein the wireless connectivity module 105 enables data transfer to mobile applications and cloud storage systems for long-term tracking of manure analysis, with built-in data security measures including:
- End-to-end encryption for all data transfers;
- Secure, anonymized cloud storage;
- User authentication for accessing stored data; and
- Regular security audits and updates.

6. The device as claimed in claim 1, wherein the user interface 104 is a touchscreen that provides real-time feedback on nutrient levels and fertilizer recommendations in an easy-to-understand format, featuring:
- Color-coded nutrient level indicators;
- Graphical representation of nutrient balance;
- Customizable recommendation display based on crop type; and
- Historical data trends and comparisons.

7. The device as claimed in claim 1, further comprising a rechargeable lithium-ion battery 106 that powers the device for field use without requiring an external power source, with specifications including:
- Capacity: 5000 mAh;
- Operating time: Up to 10 hours of continuous use;
- Charging time: 2 hours for full charge; and
- USB-C fast charging capability.

8. The device as claimed in claim 1, wherein the AI processing unit 103 continuously updates its recommendations based on new manure samples and environmental conditions, and is capable of accounting for:
- Environmental conditions (e.g., temperature, moisture);
- Manure types (solid, semi-solid, liquid); and
- Crop types and their specific fertilizer needs.

9. The device as claimed in claim 1, wherein the nutrient analysis includes pH, moisture content, and temperature data to optimize fertilizer application, with an accuracy of ±2% for major nutrients (N, P, K) and ±5% for organic matter.

10. A method for real-time manure nutrient analysis and fertilizer recommendation using the device as claimed in claim 1, comprising the steps of:
a) Collecting a manure sample and inserting it into the device's input slot;
b) Analyzing the sample using multi-spectrum sensors 102;
c) Processing the sensor data using the AI processing unit 103;
d) Generating real-time nutrient content analysis and fertilizer recommendations;
e) Displaying results on the user interface 104 and/or transferring data to external devices; and
f) Storing analysis data for future reference and continuous AI model improvement.

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