Description:FIELD OF THE INVENTION
The present invention relates to non-destructive testing and quality assessment of agricultural produce. It lies at the intersection of precision agriculture, post-harvest technology, sensor-based diagnostics, and artificial intelligence applications in crop management.
BACKGROUND OF THE INVENTION
Agricultural produce such as fruits, vegetables, and field crops are highly susceptible to various types of damage during growth, harvesting, storage, and transportation. Physical deformities, pest infestations, diseases, bruising, and environmental stress often go undetected in the early stages, leading to significant economic losses for farmers and stakeholders in the supply chain.
There is a critical need for an efficient, reliable, and real-time system that can accurately detect and assess damage in agricultural produce to support informed decision-making. Farmers and agricultural professionals require tools that can enhance productivity, reduce post-harvest losses, and ensure better quality control.
The proposed invention addresses this gap by providing a portable, smart device that utilizes digital imaging and a combination of sensors to detect external and internal damage in fruits, vegetables, and crops. By offering automated analysis and instant feedback, this invention empowers farmers with data-driven insights, enabling timely interventions and improved crop management.
EXISTING SOLUTIONS / PRIOR ART/RELATED APPLICATIONS & PATENTS:
Existing solutions include
CN110579446B: The utility model provides a fruit vegetables damage is with high spectral detection device, includes fruit vegetables rotational system, and fruit vegetables rotational system right side is equipped with fruit vegetables input conveyor, and fruit vegetables rotational system left side is equipped with fruit vegetables output conveyor, and fruit vegetables rotational system downside is equipped with and connects the work-bin, and fruit vegetables rotational system upside is equipped with high spectral detection appearance. The fruit and vegetable detection device has the advantages that fruits and vegetables are conveyed between the two discs at the rightmost end of the fruit and vegetable rotating system through the fruit and vegetable input conveyor belt, the fruits and vegetables are arranged on the upper sides of the discs at different sides, the inclined and rotating discs enable the fruits and vegetables to rotate from right to left under the action of friction, and all-dimensional detection of the fruits and vegetables is realized under the hyper-spectral detector.
RESEARCH GAP: If your device utilizes hyper-spectral imaging, it aligns with existing technologies that employ similar methods for agricultural assessment. However, your invention can be distinguished and made more impact-full by integrating additional types of sensors—such as thermal sensors for detecting temperature anomalies, moisture sensors for evaluating hydration levels, or other modalities designed to identify internal defects that may not be visible externally. This multi-sensor approach enhances the device’s diagnostic capabilities, making it more comprehensive and valuable for farmers seeking precise and early detection of crop damage.
US20170030877A1: A multi-sensor device comprises a housing containing multiple sensor modules for capturing and transmitting sensor data for plants in a crop. A control unit within the housing is operable to control the sensor modules, and a communications interface is connected to the control unit for transmitting data from said plurality of sensor modules. The sensor modules can include a physiological sensor, a surface analysis sensor, and chemical sensor. The multi-sensor device can be used as a hand-held device or mounted to a mobile platform for use in an automated crop monitoring system.
RESEARCH GAP: If your device integrates multiple sensors—such as visual, thermal, and humidity sensors—to assess both surface and internal damage in crops, it would align closely with existing platforms in the agricultural tech space. However, to stand out and offer a competitive advantage, focusing on real-time analysis capabilities and incorporating a user-friendly interface would be key. Real-time feedback can help farmers make quick, informed decisions, while an intuitive design ensures accessibility for users with varying levels of technical expertise. These features could significantly enhance the practical value and adoption of the device in precision agriculture and post-harvest quality control.
US10839503B2: A system and method for non-destructively determining characteristics of a vegetable or fruit may include processing an image of the vegetable or fruit to produce image analysis results; analyzing hyper-spectral and/or Near Infrared (NIR) illumination reflected from the vegetable or fruit to produce reflection analysis results; and calculating at least one value that reflects at least one characteristic of the vegetable or fruit based on the image analysis results and based on the reflection analysis results.
RESEARCH GAP: A device that offers a more compact and cost-effective solution, while integrating AI-driven analytics for predictive assessments, would significantly stand out from existing systems. By reducing size and cost, it becomes more accessible to a wider range of users, including small-scale farmers. The integration of AI enhances the system’s capability to not only detect current damage in fruits, vegetables, and crops but also predict potential issues before they occur. This proactive approach can help improve crop quality, reduce losses, and optimize farm management. Such a device would provide a clear technological and economic advantage over traditional systems.
US20170032258A1: Systems and methods for monitoring and assessing crop health and performance can provide rapid screening of individual plants. The systems and methods have an automated component, and rely primarily on the detection and interpretation of plant-based signals to provide information about crop health. In some cases knowledge from human experts is captured and integrated into the automated crop monitoring systems and methods. Predictive models can also be developed and used to predict future health of plants in a crop.
RESEARCH GAP: A device that emphasizes real-time monitoring with actionable insights for farmers can offer a more immediate and user-centric solution compared to traditional predictive models. By delivering instant data on crop health and potential issues, farmers can make timely decisions to mitigate risks and enhance productivity. Real-time feedback ensures that problems are addressed as they arise, reducing delays and minimizing potential damage. This hands-on approach empowers farmers with practical, easy-to-understand information directly relevant to their daily operations. As a result, such a system could be more effective and responsive, bridging the gap between technology and on-the-ground agricultural needs.
US20230143130: The present invention relates to a method for identifying fruit shelf life automatically based on thermal imaging Identifying every day the pattern of change in temperature of fruit's thermal image, we can predict the shelf life of fruit which is how many days the fruit will remain edible in an accurate manner without destructing the fruit. In this system, thermal dataset is created comprising of samples of thermal images of fruit taken on every day after harvesting where fruit may be from cold storage or room temperature using thermal imaging device. Transfer learning a deep learning technique is applied on this thermal dataset to compute the threshold weights which are then used to classify or predict the fruit shelf life by comparing these pre-trained weights with the features of fruit's thermal image extracted from convolution and poling layer of deep learning model.
RESEARCH GAP: Incorporating thermal imaging to detect crop damage aligns well with modern precision agriculture practices. However, enhancing this approach by focusing on early damage detection during the cultivation stage can provide even greater value. Identifying issues at an early stage allows for timely intervention, which can prevent crop loss and improve overall yield. Additionally, integrating the system with existing farm management platforms can streamline operations, making it easier for farmers to monitor, plan, and respond effectively. This combination of early detection and system integration would offer a more comprehensive and proactive solution, setting the device apart from standard imaging tools.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The present invention is a smart, portable device designed to detect physical damage and quality degradation in fruits, vegetables, and crops. Using digital imaging and multiple sensors, the device allows farmers and agricultural workers to quickly identify issues like bruising, cuts, rot, or pest attacks — even before they become visible to the naked eye.
The device operates by scanning the surface (and in some cases, the internal structure) of the produce and analyzing the captured data using intelligent algorithms. The results are then shown to the user through a simple interface (such as a mobile app or onboard display), offering suggestions or alerts based on the health status of the scanned item.
This tool helps farmers make quicker decisions regarding sorting, packaging, marketing, or treatment of affected produce — reducing post-harvest losses and improving product quality.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: DAMAGE DETECTION DEVICE
FIGURE 2: SYSTEM ARCHITECTURE
FIGURE 3: FLOWCHART
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
I. Non-Technical Description
This invention is a smart, portable device designed to detect physical damage and quality degradation in fruits, vegetables, and crops. Using digital imaging and multiple sensors, the device allows farmers and agricultural workers to quickly identify issues like bruising, cuts, rot, or pest attacks — even before they become visible to the naked eye.
The device operates by scanning the surface (and in some cases, the internal structure) of the produce and analyzing the captured data using intelligent algorithms. The results are then shown to the user through a simple interface (such as a mobile app or onboard display), offering suggestions or alerts based on the health status of the scanned item.
This tool helps farmers make quicker decisions regarding sorting, packaging, marketing, or treatment of affected produce — reducing post-harvest losses and improving product quality.
II. Technical Description
1. Hardware Components
? Imaging Module:
1. High-resolution RGB camera
2. Optional: Multispectral or hyperspectral camera for internal/early-stage defect detection
3. Infrared (IR) sensors for thermal imaging to detect bruising or rot
? Sensor Array:
1. Moisture sensor.
2. Gas sensor (for detecting ethylene or spoilage gases)
3. Temperature and humidity sensor (for environmental condition logging.
4. Accelerometer (if used in drone or moving platforms)
? Processing Unit:
1. Embedded microcontroller (e.g., Raspberry Pi, Arduino, or custom SoC)
2. Onboard memory and processor for data preprocessing and classification.
3. Connectivity: Wi-Fi, Bluetooth, or LoRa for data transfer and app sync.
? Power Supply:
1. Rechargeable battery with solar charging option (for field use)
? User Interface:
1. Touchscreen display or smartphone app for real-time feedback.
2. LED indicators or buzzer for immediate alerts.
2. Software and Data Processing
? Image Acquisition & Preprocessing:
1. Calibration of lighting and focus for consistent image capture.
2. Noise reduction, contrast enhancement, and object segmentation
? Damage Detection Algorithm:
1. Use of machine learning models (e.g., CNNs) trained on large datasets of damaged and healthy produce
2. Feature extraction based on color, texture, size, and shape irregularities
? Decision Engine:
1. Assigns quality grades (e.g., Healthy, Minor Damage, Major Damage)
2. Recommends actions (e.g., discard, treat, store separately)
? Data Storage & Reporting:
1. Stores data locally or on cloud for further analytics.
2. Generates inspection reports with timestamps, GPS location, and damage statistics
NOVELTY:
The novelty of your invention lies in the integration of non-invasive, multi-sensor diagnostics with intelligent analysis in a portable, user-friendly device specifically designed for early detection of quality degradation in fresh produce.
ADVANTAGES OF THE INVENTION:
1. This invention identifies bruises, rot, internal damage, or pest attacks before they become visible, enabling proactive management.
2. This invention helps prevent the spread of spoilage and disease in batches of harvested produce.
3. This invention minimizes losses due to undetected damage by ensuring timely sorting, treatment, or disposal of compromised items.
4. This invention performs assessments without harming or altering the produce, unlike some traditional testing methods.
5. This invention helps ensure only high-quality produce reaches market, leading to better prices and stronger brand trust.

, Claims:1. A portable device for non-destructive detection of physical damage and quality degradation in agricultural produce, comprising: Digital-Imaging , Artificial Intelligence , Machine Learning , IOT , Power Management Technologies , GUI , CNN , RGB Cameras .
2. The system as claimed as claim 1, wherein the imaging module configured to capture images of the surface of the produce, the imaging module comprising at least one high-resolution RGB camera and optionally a multispectral, hyperspectral, or infrared sensor.
3. The system as claimed as claim 1, wherein the processing unit comprising an embedded microcontroller or system-on-chip (SoC), the processing unit configured to:
receive and preprocess image and sensor data,
analyze the data using one or more machine learning models trained to detect signs of bruising, cuts, rot, or pest infestation.
4. The system as claimed as claim 1, wherein the user interface, comprising a touchscreen display and/or a mobile application, configured to display analysis results and provide alerts or recommendations based on the health status of the scanned produce.
5. The system as claimed as claim 1, wherein the device is configured to operate in real time, provide non-destructive assessment of agricultural produce, and enable decision-making regarding sorting, treatment, packaging, or marketing.