1. A computer-implemented method (100) for early detection of crop stress using spectral spatial deep learning and hyperspectral imagery, comprising: a. a hyperspectral imagery (102) from one or more agricultural fields using suitable sensors; b. the collected hyperspectral imagery pre-processed (104) using cleaning and normalization to improve data quality and remove noise; c. a spectral and spatial features (106) from the preprocessed (104) hyperspectral imagery (102); d. a deep learning model (108) configured to process the extracted spectral and spatial features (106); e. the deep learning model (108) using labeled data representing healthy and stressed crop conditions; f. a spectral spatial analysis mechanism within the deep learning model (108), said mechanism being configured to identify variations in spectral and spatial features (106) associated with crop stress (110); when g. a hyperspectral imagery (102) having variations in crop condition is received, said deep learning model (108) analyzes the extracted features to detect early signs of crop stress; h. a method enabling early identification of crop stress (110) before visible symptoms appear to improve decision-making; and i. wherein a method generates output (112) in the form of classification results or maps indicating stressed and healthy crop regions.

2. The method (100) as claimed in claim 1, wherein the pre-processing (104) of the hyperspectral imagery (102) comprises noise reduction and normalization to improve data consistency.

3. The method (100) as claimed in claim 1, wherein the spectral features include reflectance information across multiple wavelength bands.

4. The method (100) as claimed in claim 1, wherein the spatial features (106) include texture and structural patterns of crop regions.

5. The method (100) as claimed in claim 1, wherein said deep learning model (108) is configured to learn combined spectral and spatial representations for improved detection accuracy.

6. The method (100) as claimed in claim 1, wherein said deep learning model (108) is trained using labeled datasets comprising healthy and stressed crop samples.

7. The method (100) as claimed in claim 1, wherein said spectral-spatial analysis mechanism is configured to detect variations caused by water stress, nutrient deficiency, or disease.

8. The method (100) as claimed in claim 1, wherein said method enables detection of crop stress (110) at an early stage before visible symptoms appear.

9. The method (100) as claimed in claim 1, wherein said method is implemented in a computing system for real-time or near real-time crop monitoring.

10. The method (100) as claimed in claim 1, wherein the output (112) is generated in the form of maps or structured datasets indicating crop stress levels.

6. DATE AND SIGNATURE Dated this on 06th April 2026 Signature Mr. Srinivas Maddipati (IN/PA 3124) Agent for applicant

Description:4. DESCRIPTION
Technical Field of the Invention

The present invention relates to the field of agriculture and remote sensing. More particularly, the invention relates to a computer-implemented method for early detection of crop stress using spectral-spatial deep learning and hyperspectral imagery.

Background of the Invention

Crop health monitoring is important for improving agricultural productivity and ensuring food security. Farmers usually rely on visual inspection to identify crop stress, but such methods often fail to detect issues at an early stage. By the time visible symptoms appear, the damage may already be significant.

Hyperspectral imagery provides detailed information across multiple wavelength bands, allowing detection of subtle changes in plant condition. These changes may indicate stress due to factors such as water deficiency, disease, or nutrient imbalance. However, handling and analysing such high-dimensional data is challenging.

Existing approaches use machine learning or deep learning models for crop analysis, but many focus only on spectral information. Ignoring spatial patterns limits the model’s ability to accurately capture the condition of crops in real-world scenarios.

Many conventional models do not perform well under varying environmental conditions such as changes in lighting, soil type, or weather. This reduces their reliability when applied across different agricultural regions.

Accordingly, there exists a need for a method that can effectively combine spectral and spatial information to detect crop stress at an early stage. Such a method should provide accurate, reliable, and timely results to support better agricultural decision-making.
Objects of the Invention

The principal object of the present invention is to provide a method for early detection of crop stress using spectral spatial deep learning and hyperspectral imagery.

Another object of the present invention is to utilize both spectral and spatial features for improving the accuracy of crop stress detection.

Another object of the present invention is to detect crop stress at an early stage before visible symptoms appear in crops.

Another object of the present invention is to handle high dimensional hyperspectral data in an efficient manner.

Another object of the present invention is to improve the robustness and reliability of the model under varying environmental conditions.

Another object of the present invention is to provide a method suitable for real-time or near real-time agricultural monitoring.

Another object of the present invention is to assist farmers and stakeholders in taking timely decisions to reduce crop loss and improve yield.

Brief Summary of the Invention

The present invention provides a method for early detection of crop stress using spectral-spatial deep learning and hyperspectral imagery. The method is designed to identify crop stress at an early stage before visible symptoms appear. This helps in reducing potential crop loss and supports better agricultural management.
In one embodiment, hyperspectral imagery is collected from agricultural fields using suitable sensors. The collected data contains detailed spectral information across multiple wavelength bands. This rich data enables detection of subtle changes in plant condition that are not visible through conventional imaging.

The collected data is then preprocessed to remove noise and improve consistency. This step ensures that the data is suitable for further analysis and reduces errors in the model. Proper preprocessing improves the reliability of the overall detection process.

After preprocessing, spectral and spatial features are extracted from the data. These features help in capturing both the chemical properties and structural patterns of crops. The combined use of these features enhances the ability to distinguish between healthy and stressed crops.

A deep learning model is then designed and trained using labeled data representing healthy and stressed crop conditions. The model learns to identify variations associated with crop stress. This training improves the model’s ability to generalize across different field conditions.

The method analyzes the extracted features to detect early signs of stress and generates output in the form of classification results or maps. This enables timely intervention and improves agricultural productivity. The output can be used by farmers and stakeholders for informed decision-making.

Brief Description of the Drawings

The invention will be further understood from the following detailed description of a preferred embodiment taken in conjunction with an appended drawing, in which:

Fig. 1A illustrates a flow diagram of a method (100) for early detection of crop stress using spectral-spatial deep learning and hyperspectral imagery, in accordance with an exemplary embodiment of the present invention;

Fig. 2 illustrates a functional representation of spectral-spatial analysis and crop stress detection using hyperspectral imagery, in accordance with an exemplary embodiment of the present invention;

Detailed Description of the Invention

It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. In addition, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

The use of “including”, “comprising” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. Further, the use of terms “first”, “second”, and “third”, and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

According to an exemplary embodiment of the present invention, a method for early detection of crop stress using spectral-spatial deep learning and hyperspectral imagery is disclosed. The method focuses on identifying stress conditions in crops at an early stage to support timely intervention and improve agricultural outcomes.

In accordance with an exemplary embodiment of the present invention, hyperspectral imagery is collected from agricultural fields using suitable sensors mounted on platforms such as drones or satellites. The collected data includes multiple spectral bands that capture detailed information about crop condition.
In accordance with an exemplary embodiment of the present invention, the collected hyperspectral data is subjected to pre-processing. The pre-processing includes cleaning, normalization, and noise reduction to improve data quality and ensure consistency across different samples.

In accordance with an exemplary embodiment of the present invention, spectral and spatial features are extracted from the pre-processed data. The spectral features provide information about the chemical and physiological properties of crops, while spatial features capture structural and textural patterns.

In accordance with an exemplary embodiment of the present invention, a deep learning model is designed and implemented to process the extracted features. The model architecture is configured to effectively combine spectral and spatial information for improved analysis.

In accordance with an exemplary embodiment of the present invention, the deep learning model is trained using labelled datasets representing healthy and stressed crop conditions. The training process enables the model to learn patterns associated with different types of stress.

In accordance with an exemplary embodiment of the present invention, a spectral-spatial analysis mechanism is incorporated within the model. This mechanism helps in identifying variations in input data that indicate early signs of crop stress.

In accordance with an exemplary embodiment of the present invention, the method analyses changes in spectral signatures and spatial patterns caused by factors such as water deficiency, nutrient imbalance, or disease. These changes are detected before visible symptoms appear.
In accordance with an exemplary embodiment of the present invention, the method generates output in the form of classification results or maps indicating stressed and healthy crop regions. The output can be used for monitoring and decision-making.

In accordance with an exemplary embodiment of the present invention, the method improves detection accuracy and reliability under varying environmental conditions. It can adapt to differences in lighting, soil, and weather conditions.

In accordance with an exemplary embodiment of the present invention, the method supports real-time or near real-time implementation for continuous crop monitoring. This enables farmers and stakeholders to take timely actions and improve crop yield.

Fig. 1A illustrates the method (100) includes a hyperspectral imaging (102) unit configured to collect data from agricultural fields, wherein the collected data is provided to a preprocessing (104) module for cleaning and normalization. The preprocessed data is then supplied to a spectral-spatial analysis unit, which extracts spectral features (106) based on wavelength information and spatial features representing structural patterns. The extracted features are processed by a deep learning model (108) configured to analyze variations associated with crop stress (110). The system generates output (112) in the form of stress maps and classification results indicating different crop conditions, thereby enabling early detection of crop stress (110).

Fig. 1B illustrates the hyperspectral data obtained from agricultural fields is analyzed to derive spectral features based on wavelength information and spatial features (106) representing crop structure. These features are provided to a deep learning model (108) configured to learn patterns associated with crop stress (110) conditions. The model processes the combined spectral and spatial information to identify variations indicating stress. Based on this analysis, output (112) is generated in the form of stress maps and classification results, enabling identification of different crop conditions. , Claims:CLAIMS
I/We Claim:
1. A computer-implemented method (100) for early detection of crop stress using spectral spatial deep learning and hyperspectral imagery, comprising:
a. a hyperspectral imagery (102) from one or more agricultural fields using suitable sensors;
b. the collected hyperspectral imagery pre-processed (104) using cleaning and normalization to improve data quality and remove noise;
c. a spectral and spatial features (106) from the preprocessed (104) hyperspectral imagery (102);
d. a deep learning model (108) configured to process the extracted spectral and spatial features (106);
e. the deep learning model (108) using labeled data representing healthy and stressed crop conditions;
f. a spectral spatial analysis mechanism within the deep learning model (108), said mechanism being configured to identify variations in spectral and spatial features (106) associated with crop stress (110); when
g. a hyperspectral imagery (102) having variations in crop condition is received, said deep learning model (108) analyzes the extracted features to detect early signs of crop stress;
h. a method enabling early identification of crop stress (110) before visible symptoms appear to improve decision-making; and
i. wherein a method generates output (112) in the form of classification results or maps indicating stressed and healthy crop regions.

2. The method (100) as claimed in claim 1, wherein the pre-processing (104) of the hyperspectral imagery (102) comprises noise reduction and normalization to improve data consistency.
3. The method (100) as claimed in claim 1, wherein the spectral features include reflectance information across multiple wavelength bands.

4. The method (100) as claimed in claim 1, wherein the spatial features (106) include texture and structural patterns of crop regions.

5. The method (100) as claimed in claim 1, wherein said deep learning model (108) is configured to learn combined spectral and spatial representations for improved detection accuracy.

6. The method (100) as claimed in claim 1, wherein said deep learning model (108) is trained using labeled datasets comprising healthy and stressed crop samples.

7. The method (100) as claimed in claim 1, wherein said spectral-spatial analysis mechanism is configured to detect variations caused by water stress, nutrient deficiency, or disease.

8. The method (100) as claimed in claim 1, wherein said method enables detection of crop stress (110) at an early stage before visible symptoms appear.

9. The method (100) as claimed in claim 1, wherein said method is implemented in a computing system for real-time or near real-time crop monitoring.

10. The method (100) as claimed in claim 1, wherein the output (112) is generated in the form of maps or structured datasets indicating crop stress levels.

6. DATE AND SIGNATURE
Dated this on 06th April 2026
Signature

Mr. Srinivas Maddipati
(IN/PA 3124)
Agent for applicant