VISION-BASED ADDON SOLUTION FOR CONVENTIONAL TOMATO HARVESTERS WITH AI-BASED SUGGESTIONS

Abstract

Vision-Based Addon Solution for Conventional Tomato Harvesters with AI-Based Suggestions This invention presents a vision-based addon solution for enhancing conventional tomato harvesting techniques. Utilizing advanced cameras and AI/ML algorithms, this system enables real-time monitoring of tomato quantity and quality, providing operators with instant feedback and AI-based suggestions for optimal decision-making. The Raspberry Pi Processor Board serves as the system's core, integrating components such as a Camera Module for image capture, a Neural Stick for rapid AI processing, an HMI Display for visual notifications, and a Speaker for auditory alerts. With internet connectivity for remote monitoring, this solution offers an efficient and user-friendly approach to improve accuracy, responsiveness, and overall performance in tomato harvesting operations.

Specification

Description:FIELD OF THE INVENTION
This invention relates to Vision-Based Addon Solution for Conventional Tomato Harvesters with AI-Based Suggestions.
BACKGROUND OF THE INVENTION
The novel vision-based technology is revolutionizing conventional tomato picking techniques. This system allows for real-time monitoring of tomato quantity and quality by utilizing cutting-edge cameras and AI/ML technology. Through a user-friendly display, operators are notified swiftly and given alternatives, enabling them to make informed decisions immediately. Furthermore, timely notifications are guaranteed by the integrated audio component, which enhances overall responsiveness during the harvesting process.
The conventional approach to tomato harvesting faces challenges related to inefficiency and lack of real-time quality assessment. Harvest management is not best when counting and evaluating by hand because it takes a lot of time and is prone to mistakes. Moreover, operators trying to make prompt judgments during the harvesting process have a significant challenge due to the lack of instantaneous feedback on tomato quality.
CN102523825B - The present invention relates to a kind of tomato harvester, comprising road wheel, mainframe, mainframe front portion is provided with picking head, Primary Transmit device is provided with after described picking head, Primary Transmit device rear portion is provided with fruit separating device, described fruit separating device bottom is provided with secondary transport device, lateral transport device is provided below described secondary transport device, lateral transport device promotes transmitting device by fruit and is connected with look screening device, described look screening device rear portion is provided with manually picks up conveyer belt, manually pick up conveyer belt rear portion and be provided with fruit elevator telescopic arm, triangle longitudinal balance parts are provided between described look screening device and mainframe, the present invention is rational in infrastructure, working stability, economical and practical, it is a kind of tomato harvester that can carry out harvest operation well. Vision-Based Addon Solution to upgrade Conventional Tomato Harvesters is the novelty of the system.
US3618617A - A separator for mechanical harvesters of the type where the fruit or other heavy part of the crop is separated from the remainder of the plant by a shaking action. A generally rectangular frame has a front end where harvested plants are introduced and a rear end where they leave. A rockshaft supports the front end of the frame for substantially pure fore-and-aft movement, while crank means support the rear end of the frame and drive it as a unit in a generally circular motion involving both vertical and fore-and-aft components. There is a plurality of pulleys at each end of the frame, those on the rear end being mounted on the crank pin and driven by the crank means, those on the front end being idlers individually spring mounted. The pulleys carry a plurality of closed-loop belts with upwardly extending projections for engaging harvesting plants and moving them from the front to the rear of the frame. A collecting conveyor beneath the frame catches the fruit shaken from said plant and conveys it elsewhere. Thus, the more easily loosened fruit is shaken off by fore-and-aft shaking while the fruit more difficult to loosen is shaken off by the gradual introduction of vertical movement as the crop moves rearwardly. The belt, pulleys, and belt guides are also novel in structure. Vision-Based Addon Solution to upgrade Conventional Tomato Harvesters is the novelty of the system.
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.
Disclosed herein 1. A vision-based addon system for conventional tomato harvesters comprising VBASTMNode (200)-which is outfitted with a Raspberry Pi Processor Board, Camera Module (400), Neural Stick (300), Speaker (700), HMI Display (500), and Power Supply (600). A Camera Module captures live images of tomatoes passing through the harvester, allowing AI and ML algorithms to analyze and provide real-time tomato count and quality assessment. A Neural Stick for accelerated processing of AI and ML algorithms, facilitating rapid analysis of captured images for efficient tomato counting and quality evaluation. An HMI Display that serves as a visual interface, presenting operators with real-time alerts, tomato counts, and quality evaluations for informed decision-making during the harvesting process. An integrated Speaker that provides audio notifications, ensuring operators are promptly alerted to critical information even when not actively monitoring the display.
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 VBASTMNode is an integrated system that uses cutting-edge vision-based technologies to improve conventional tomato harvesters. Fundamentally, the technology optimizes the harvesting process by combining advanced algorithms with a variety of hardware components. The Raspberry Pi Processor Board serves as the system's central processing unit, coordinating the operations of several parts. One important component is the Camera Module, which records live photos of tomatoes passing through the harvester. These photos are processed using Neural Stick-based AI and ML algorithms. The algorithms are mostly used to count the tomatoes that travel through the harvester and evaluate the quality of each tomato individually. This dual capacity allows for a more accurate and effective collection process by giving an extensive picture of the harvest.

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: SYSTEM ARCHITECTURE
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.
The VBASTMNode is an integrated system that uses cutting-edge vision-based technologies to improve conventional tomato harvesters. Fundamentally, the technology optimizes the harvesting process by combining advanced algorithms with a variety of hardware components. The Raspberry Pi Processor Board serves as the system's central processing unit, coordinating the operations of several parts. One important component is the Camera Module, which records live photos of tomatoes passing through the harvester. These photos are processed using Neural Stick-based AI and ML algorithms. The algorithms are mostly used to count the tomatoes that travel through the harvester and evaluate the quality of each tomato individually. This dual capacity allows for a more accurate and effective collection process by giving an extensive picture of the harvest.
The HMI Display presents real-time alarms and recommendations produced by the AI algorithms and acts as a visual interface for both operators and authorities. Harvest progress, tomato counts, and quality evaluations are all visible to operators on the display, enabling them to make informed decisions in real time to maximize the harvesting process. Concurrently, the linked Speaker gives the system an audio component. The VBASTMNode makes sure operators are promptly alerted to crucial information, even while they are not actively watching the HMI Display, by means of integrated auditory announcements. This feature improves the harvesting process's responsiveness and enables prompt intervention in the event that problems or anomalies are found. The VBASTMNode can also be connected to the internet, which offers further features and the ability to monitor remotely. Real-time data transfer is made possible by this connectivity, giving authorities and operators the ability to acquire information and make deft judgments from any place.
BEST METHOD OF WORKING
A vision-based addon solution for conventional tomato harvesters comprising a Raspberry Pi Processor Board that serves as the central processing unit, enabling the coordination of various hardware components for optimized tomato harvesting.
A vision-based addon solution with a Camera Module that captures live images of tomatoes passing through the harvester, allowing AI and ML algorithms to analyze and provide real-time tomato count and quality assessment.
A vision-based addon solution further comprising a Neural Stick for accelerated processing of AI and ML algorithms, facilitating rapid analysis of captured images for efficient tomato counting and quality evaluation.
A vision-based addon solution incorporating an HMI Display that serves as a visual interface, presenting operators with real-time alerts, tomato counts, and quality evaluations for informed decision-making during the harvesting process.
A vision-based addon solution including an integrated Speaker that provides audio notifications, ensuring operators are promptly alerted to critical information even when not actively monitoring the display.
A vision-based addon solution with internet connectivity to enable remote monitoring and real-time data transfer, allowing operators and authorities to access harvest information from any location.
A vision-based addon solution with advanced algorithms enabling automated tomato counting, real-time quality checks, and immediate feedback, significantly enhancing the efficiency and decision-making processes involved in traditional tomato harvesting.
ADVANTAGES OF THE INVENTION
1. The core technology of this breakthrough is the VBASTMNode, which uses sophisticated vision-based capabilities to automate tomato counting, perform real-time quality checks, and provide operators with instant feedback. This greatly improves decision-making and efficiency in conventional tomato harvesting.
2. The Camera Module, which takes pictures of tomatoes in real time while they are being harvested, is a key component of this innovation. It makes it possible for sophisticated AI and ML algorithms to evaluate the photos and provide precise tomato counts and quality evaluation. This makes a significant contribution to the accuracy and efficiency of traditional tomato harvesting.
3. The Neural Stick plays a crucial part in this innovation by processing AI and ML algorithms more quickly. This acceleration makes it possible to analyze photos taken by the Camera Module quickly and precisely, which increases the efficiency of conventional tomato harvesting by enabling real-time tomato counting and quality assessment.
4. By providing audio announcements, the Speaker plays a crucial part in this innovation by enabling operators to get alerts and ideas right away when harvesting tomatoes. This improves decision-making and overall responsiveness.
5. This innovation's key interface is the HMI Display, which shows quality evaluations, data on tomato counts, and real-time alerts. This increases overall efficiency and management of the harvesting process by enabling operators to make well-informed decisions during conventional tomato harvesting.
, Claims:1. A vision-based addon system for conventional tomato harvesters comprising VBASTMNode (200)-which is outfitted with a Raspberry Pi Processor Board, Camera Module (400), Neural Stick (300), Speaker (700), HMI Display (500), and Power Supply (600).
2. The vision-based addon system as claimed in Claim 1, wherein a Camera Module captures live images of tomatoes passing through the harvester, allowing AI and ML algorithms to analyze and provide real-time tomato count and quality assessment.
3. The vision-based addon system as claimed in Claim 1, further comprising a Neural Stick for accelerated processing of AI and ML algorithms, facilitating rapid analysis of captured images for efficient tomato counting and quality evaluation.
4. The vision-based addon system as claimed in Claim 1, incorporating an HMI Display that serves as a visual interface, presenting operators with real-time alerts, tomato counts, and quality evaluations for informed decision-making during the harvesting process.
5. The vision-based addon system as claimed in Claim 1, including an integrated Speaker that provides audio notifications, ensuring operators are promptly alerted to critical information even when not actively monitoring the display.
6. The vision-based addon system as claimed in Claim 1, wherein internet connectivity is provided to enable remote monitoring and real-time data transfer, allowing operators and authorities to access harvest information from any location.
7. The vision-based addon system as claimed in Claim 1, with advanced algorithms enabling automated tomato counting, real-time quality checks, and immediate feedback, significantly enhancing the efficiency and decision-making processes involved in traditional tomato harvesting.

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