1,281 research outputs found
Artificial Neural Network based Model for Fruit Grade Empirical Thresholding and Feature Extraction based Back Propagation
This study details a novel attribute retrieval method for use in pre-processing images, and then applies it to the development of an "artificial neural network" system based on back propagation for identifying fruits in photographs. The βScale Conjugate Gradientβ (SCG) technique is used For back propagation. In this paper, there are three stages to the process. First, MATLAB was used to process a variety of external image-based apple properties. Since merely colour is insufficient to judge the quality, size and weight characteristics were also taken into consideration. Second, features extraction was carried out during picture pre-processing to simplify the method by concentrating only on important features. The Support Vector Machine (SVM) algorithm is a favourite for creating classification models that are relatively small in weight. The classification in this work is done using the MATLAB-ANN (Artificial Neural Network) toolkit. A single hidden layer BP-ANN (Back propagation- artificial neural network) was employed with sigmoid activation functions,. The outcome was determined by the appropriate output variables, which is the apple's quality class, which was determined to be Class A, Class B, Class C, and Class D, respectively. The modeling result indicates the tremendous match between the data used in training and assumed output values. It also has shorter calculation time due to the SCG algorithm. It is also possible for apple producers and distributors to classify their fruit using this model and reduce the cost by avoiding manual classification
Applications of Image Processing for Grading Agriculture products
Image processing in the context of Computer vision, is one of the renowned topic of computer science and engineering, which has played a vital role in automation. It has eased in revealing unknown fact in medical science, remote sensing, and many other domains. Digital image processing along with classification and neural network algorithms has enabled grading of various things. One of prominent area of its application is classification of agriculture products and especially grading of seed or cereals and its cultivars. Grading and sorting system allows maintaining the consistency, uniformity and depletion of time. This paper highlights various methods used for grading various agriculture products.
DOI: 10.17762/ijritcc2321-8169.15036
Neural Network for Papaya Leaf Disease Detection
The scientific name of papaya is Carica papaya which is an herbaceous perennial in the family Caricaceae grown for its edible fruit. The papaya plant is tree-like,usually unbranched and has hollow stems and petioles. Its origin is Costa Rica, Mexico and USA. The common names of papaya is pawpaw and tree melon. In East Indies and Southern Asia, it is known as tapaya, kepaya, lapaya and kapaya. In Brazil,it is known as Mamao. Papayas are a soft, fleshy fruit that can be used in a wide variety of culinary ways. The possible health benefits of consuming papaya include a reduced risk of heart disease, diabetes, cancer, aiding in digestion, improving blood glucose control in people with diabetes, lowering blood pressure, and improving wound healing.
Disease identification in early stage can increase crop productivity and hence lead to economical growth. This work deals with leaf rather than fruit. Images of papaya leaf samples, image compression and image filtering and several image generation techniques are used to obtain several trained data image sets and then hence providing a better product. This paper focus on the power of neural network for detecting diseases in the papaya. Image segmentation is done with the help of k-medoid clustering algorithm which is a partitioning based clustering method
GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer
This thesis is divided into two parts:Part I: Analysis of Fruits, Vegetables, Cheese and Fish based on Image Processing using Computer Vision and Deep Learning: A Review. It consists of a comprehensive review of image processing, computer vision and deep learning techniques applied to carry out analysis of fruits, vegetables, cheese and fish.This part also serves as a literature review for Part II.Part II: GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer. This part introduces to an end-to-end deep neural network architecture that can predict the degree of acceptability by the consumer for a guava based on sensory evaluation
A Novel Tomato Volume Measurement Method based on Machine Vision
Density is one of the auxiliary indicators for judging the internal quality of tomatoes. However, in the density measurement process, it is often difficult to measure the volume of the tomatoes accurately. To solve this problem, first, this study proposed a novel tomato volume measurement method based on machine vision. The proposed method uses machine vision to measure the geometric feature parameters of tomatoes, and inputs them into the LabVIEW software to convert the calculation of irregular tomato volume into a BP neural network (BPNN) model that calculates the plane pixel area and pixel volume, thereby realizing the modeling, analysis, design and simulation of tomato volume; then, an experimental platform was constructed to compare the results of the proposed method with the results predicted by the 3D wireframe model. When the number of photos taken was n = 5, the average error of the tomato volume prediction results of the 3D wireframe model was 8.22%, and the highest accuracy was 92.93%; while the average error of the tomato volume prediction results of the BPNN was 4.60%, and the highest accuracy was 95.60%. Increasing the number of orthographic projections can improve the accuracy of the model, but when the number of photos was more than 7, the accuracy improvement was not significant. Also, increasing the number of nodes in the hidden layer can improve the accuracy of the model, however, considering that increasing the number of nodes will increase the host operating cost, it is suggested to choose a node number of 12 for the tomato volume measurement. In the end, the final experimental results showed that the proposed method achieved better measurement results. However, the volume measured by the two models is larger than the real volume of tomatoes. For this reason, we added a correction coefficient to the BPNN model, and its highest accuracy has increased by 1.3%
SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique
Computers have been used for mechanization and
automation in different applications of agriculture/horticulture.
The critical decision on the agricultural yield and plant protection
is done with the development of expert system (decision support
system) using computer vision techniques. One of the areas
considered in the present work is the processing of images of
plant diseases affecting agriculture/horticulture crops. The first
symptoms of plant disease have to be correctly detected, identified,
and quantified in the initial stages. The color and texture features
have been used in order to work with the sample images of plant
diseases. Algorithms for extraction of color and texture features
have been developed, which are in turn used to train support
vector machine (SVM) and artificial neural network (ANN)
classifiers. The study has presented a reduced feature set based
approach for recognition and classification of images of plant
diseases. The results reveal that SVM classifier is more suitable
for identification and classification of plant diseases affecting
agriculture/horticulture crops
ΠΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΠ΅ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈ ΡΠΎΠ±ΠΎΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ ΠΌΠ°ΡΠΈΠ½Ρ Π΄Π»Ρ Π²ΠΎΠ·Π΄Π΅Π»ΡΠ²Π°Π½ΠΈΡ ΡΠ°Π΄ΠΎΠ²ΡΡ ΠΊΡΠ»ΡΡΡΡ
The existing models of industrial robots cannot perform technological processes of apple harvesting. It is noted that there is a need for developing special actuators, grippers and new control algorithms for harvesting horticulture products. (Research purpose) The research aimed to develop an intelligent control system for horticulture industrial technologies and robotic techniques for yield monitoring and fruit harvesting. (Materials and methods) The research methodology was based on such modern methods as computer modeling and programming. In particular, the following methods were applied: systems analysis, artificial neural networks theory, pattern recognition, digital signal processing. The development of software, hardware and software was carried out in accordance with the requirements of GOST technical standards. The following programming languages were used: (C / Cβ
++)-basedΒ OpenCV library, Spyder Python Development Environment, PyTorch and Flask frameworks, and JavaScript. Image marking for training neural networks was carried out via VGG ImageAnnotator and in Labelbox. The design process was based on the finite element method, CAD SolidWorks software environment. (Results and discussion) An intelligent management system for horticulture industrial technologies has been created based the on the Β«Agrointellect VIMΒ» hardware and software complex. The concept of the system is shown to be implemented via computer and communication technology, robotic machines, the software for collecting, organizing, analyzing and storing data. The gripper proves to fix an apple gently and holds it securely. Depending on the size, the fruit fixation time is 1.5-2.0 seconds, the fruit maximum size is 85 per 80 millimeters , and its maximum weight is 500 grams. (Conclusions) The developed intelligent control system for industrial technologies based on Β«Agrointellect VIMΒ» hardware and software complex ensures the efficient real-time processing of information necessary for the design of intelligent agricultural technologies using robotic machines and artificial intelligence systems.ΠΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΡΡ
ΡΠΎΠ±ΠΎΡΠΎΠ² Π½Π΅ ΠΌΠΎΠ³ΡΡ Π²ΡΠΏΠΎΠ»Π½ΡΡΡ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΡΠΎΡΠ΅ΡΡΡ ΡΠ±ΠΎΡΠΊΠΈ ΡΡΠΎΠΆΠ°Ρ ΡΠ±Π»ΠΎΠΊ. ΠΡΠΌΠ΅ΡΠΈΠ»ΠΈ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΡ
ΠΈΡΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΡΡΡΠΎΠΉΡΡΠ², Π·Π°Ρ
Π²Π°ΡΠ½ΡΡ
ΠΏΡΠΈΡΠΏΠΎΡΠΎΠ±Π»Π΅Π½ΠΈΠΉ ΠΈ Π½ΠΎΠ²ΡΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ Π΄Π»Ρ ΡΠ±ΠΎΡΠ° ΡΡΠΎΠΆΠ°Ρ Π² ΡΠ°Π΄ΠΎΠ²ΠΎΠ΄ΡΡΠ²Π΅. (Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ) Π Π°Π·ΡΠ°Π±ΠΎΡΠ°ΡΡ ΡΠΈΡΡΠ΅ΠΌΡ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΡΠΌΠΈ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠΌΠΈ Π² ΡΠ°Π΄ΠΎΠ²ΠΎΠ΄ΡΡΠ²Π΅ ΠΈ ΡΠΎΠ±ΠΎΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΡΠ΅Π΄ΡΡΠ²Π° Π΄Π»Ρ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΡΠΎΠΆΠ°ΠΉΠ½ΠΎΡΡΠΈ ΠΈ ΡΠ±ΠΎΡΠ° ΠΏΠ»ΠΎΠ΄ΠΎΠ². (ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ) ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ. ΠΡΠΈΠΌΠ΅Π½ΠΈΠ»ΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°, ΡΠ΅ΠΎΡΠΈΡ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΡΡ
Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ, ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΠ΅ ΠΎΠ±ΡΠ°Π·ΠΎΠ², ΡΠΈΡΡΠΎΠ²ΡΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΡ ΡΠΈΠ³Π½Π°Π»ΠΎΠ². Π Π°Π·ΡΠ°Π±ΠΎΡΠΊΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎ-Π°ΠΏΠΏΠ°ΡΠ°ΡΠ½ΡΡ
ΡΡΠ΅Π΄ΡΡΠ² ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΡΠΌΠΈ ΠΠΠ‘Π’. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈ ΡΠ·ΡΠΊΠΈ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π‘/Π‘++ Ρ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠΎΠΉ OpenCV, Python-ΡΡΠ΅Π΄Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ Spyder, ΡΡΠ΅ΠΉΠΌΠ²ΠΎΡΠΊ PyTorch ΠΈ Flask, Π° ΡΠ°ΠΊΠΆΠ΅ JavaScript. Π Π°Π·ΠΌΠ΅ΡΠΊΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π΄Π»Ρ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ ΠΏΡΠΎΠ²Π΅Π»ΠΈ Π² VGG ImageAnnotator ΠΈ Π² Labelbox. ΠΡΠΈ ΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΎΠΏΠ΅ΡΠΈΡΠΎΠ²Π°Π»ΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΊΠΎΠ½Π΅ΡΠ½ΡΡ
ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ², ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠΉ ΡΡΠ΅Π΄ΠΎΠΉ Π‘ΠΠΠ SolidWorks. (Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅) Π‘ΠΎΠ·Π΄Π°Π»ΠΈ ΡΠΈΡΡΠ΅ΠΌΡ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΡΠΌΠΈ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠΌΠΈ Π² ΡΠ°Π΄ΠΎΠ²ΠΎΠ΄ΡΡΠ²Π΅ Π½Π° Π±Π°Π·Π΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎ-Π°ΠΏΠΏΠ°ΡΠ°ΡΠ½ΠΎΠ³ΠΎ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ° Β«ΠΠ³ΡΠΎΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡ ΠΠΠΒ». ΠΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠΈΡ ΡΠΈΡΡΠ΅ΠΌΡ ΡΠ΅Π°Π»ΠΈΠ·ΡΠ΅ΡΡΡ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΠΈ ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠ΅Ρ
Π½ΠΈΠΊΠΈ, ΡΠΎΠ±ΠΎΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΌΠ°ΡΠΈΠ½, ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ Π΄Π»Ρ ΡΠ±ΠΎΡΠ°, ΡΠΈΡΡΠ΅ΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΠΈ, Π°Π½Π°Π»ΠΈΠ·Π° ΠΈ Ρ
ΡΠ°Π½Π΅Π½ΠΈΡ Π΄Π°Π½Π½ΡΡ
. ΠΠΏΡΠ΅Π΄Π΅Π»ΠΈΠ»ΠΈ, ΡΡΠΎ Π·Π°Ρ
Π²Π°Ρ Π°ΠΊΠΊΡΡΠ°ΡΠ½ΠΎ ΡΠΈΠΊΡΠΈΡΡΠ΅Ρ ΡΠ±Π»ΠΎΠΊΠΎ ΠΈ Π½Π°Π΄Π΅ΠΆΠ½ΠΎ ΡΠ΄Π΅ΡΠΆΠΈΠ²Π°Π΅Ρ Π΅Π³ΠΎ. ΠΡΠ΅ΠΌΡ Π½Π° ΡΠΈΠΊΡΠ°ΡΠΈΡ ΠΏΠ»ΠΎΠ΄Π° Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ ΡΠ°Π·ΠΌΠ΅ΡΠ° ΡΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ 1,5-2,0 ΡΠ΅ΠΊΡΠ½Π΄Ρ, ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΡΠ΅ ΡΠ°Π·ΠΌΠ΅ΡΡ ΠΏΠ»ΠΎΠ΄Π° β 85 Π½Π° 80 ΠΌΠΈΠ»Π»ΠΈΠΌΠ΅ΡΡΠΎΠ², Π° Π΅Π³ΠΎ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΡΠΉ Π²Π΅Ρ β 500 Π³ΡΠ°ΠΌΠΌΠΎΠ². (ΠΡΠ²ΠΎΠ΄Ρ) Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΠΉ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎ-Π°ΠΏΠΏΠ°ΡΠ°ΡΠ½ΡΠΉ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡ ΡΠΈΡΡΠ΅ΠΌΡ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΡΠΌΠΈ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠΌΠΈ Β«ΠΠ³ΡΠΎΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡ ΠΠΠΒ» ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°Π΅Ρ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΡΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΡ Π² ΡΠ΅Π°Π»ΡΠ½ΠΎΠΌ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ, Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΠΉ Π΄Π»Ρ ΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
Π°Π³ΡΠΎΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΡΠΎΠ±ΠΎΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΌΠ°ΡΠΈΠ½ ΠΈ ΡΠΈΡΡΠ΅ΠΌ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ°
Computer Vision System for Non-Destructive and Contactless Evaluation of Quality Traits in Fresh Rocket Leaves (Diplotaxis Tenuifolia L.)
La tesi di dottorato Γ¨ incentrata sull'analisi di tecnologie non distruttive per il controllo della
qualitΓ dei prodotti agroalimentari, lungo l'intera filiera agroalimentare. In particolare, la tesi
riguarda l'applicazione del sistema di visione artificiale per valutare la qualitΓ delle foglie di
rucola fresh-cut. La tesi Γ¨ strutturata in tre parti (introduzione, applicazioni sperimentali e
conclusioni) e in cinque capitoli, rispettivamente il primo e il secondo incentrati sulle
tecnologie non distruttive e in particolare sui sistemi di computer vision per il monitoraggio
della qualitΓ dei prodotti agroalimentari. Il terzo, quarto e quinto capitolo mirano a valutare le
foglie di rucola sulla base della stima di parametri qualitativi, considerando diversi aspetti: (i)
la variabilitΓ dovuta alle diverse pratiche agricole, (ii) la senescenza dei prodotti confezionati
e non, e (iii) lo sviluppo e sfruttamento dei vantaggi di nuovi modelli piΓΉ semplici rispetto al
machine learning utilizzato negli esperimenti precedenti. Il lavoro di ricerca di questa tesi di
dottorato Γ¨ stato svolto dall'UniversitΓ di Foggia, dall'Istituto di Scienze delle Produzioni
Alimentari (ISPA) e dall'Istituto di Tecnologie e Sistemi Industriali Intelligenti per le
Manifatture Avanzate (STIIMA) del Consiglio Nazionale delle Ricerche (CNR). LβattivitΓ di
ricerca Γ¨ stata condotta nell'ambito del Progetto SUS&LOW (Sustaining Low-impact Practices
in Horticulture through Non-destructive Approach to Provide More Information on Fresh
Produce History & Quality), finanziato dal MUR-PRIN 2017, e volto a sostenere la qualitΓ
della produzione e dell'ambiente utilizzando pratiche agricole a basso input e la valutazione
non distruttiva della qualitΓ di prodotti ortofrutticoli.The doctoral thesis focused on the analysis of non-destructive technologies available for the
control quality of agri-food products, along the whole supply chain. In particular, the thesis
concerns the application of computer vision system to evaluate the quality of fresh rocket
leaves. The thesis is structured in three parts (introduction, experimental applications and
conclusions) and in 5 chapters, the first and second focused on non-destructive technologies
and in particular on computer vision systems for monitoring the quality of agri-food products,
respectively. The third, quarter, and fifth chapters aim to assess the rocket leaves based on the
estimation of quality aspects, considering different aspects: (i) the variability due to the
different agricultural practices, (ii) the senescence of packed and unpacked products, and (iii)
development and exploitation of the advantages of new models simpler than the machine
learning used in the previous experiments. The research work of this doctoral thesis was carried
out by the University of Foggia, the Institute of Science of Food Production (ISPA) and the
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing
(STIIMA) of National Research Council (CNR). It was conducted within the Project
SUS&LOW (Sustaining Low-impact Practices in Horticulture through Non-destructive
Approach to Provide More Information on Fresh Produce History & Quality), funded by MUR-
PRIN 2017, and aimed at sustaining quality of production and of the environment using low
input agricultural practices and non-destructive quality evaluation
Importance of Machine Vision Framework with Nondestructive Approach for Fruit Classification and Grading: A Review
Machine vision technology has gained significant importance in the agricultural industry, particularly in the non-destructive classification and grading of fruits. This paper presents a comprehensive review of the existing literature, highlighting the crucial role of machine vision in automating the fruit quality assessment process. The study encompasses various aspects, including image acquisition techniques, feature extraction methods, and classification algorithms. The analysis reveals the substantial progress made in the field, such as developing sophisticated hardware and software solutions, which have improved accuracy and efficiency in fruit grading. Furthermore, it discusses the challenges and limitations, such as dealing with variability in fruit appearance, handling different fruit types, and real-time processing. The identification of future research needs emphasizes the potential for enhancing machine vision frameworks through the integration of advanced technologies like deep learning and artificial intelligence.Additionally, it underscores the importance of addressing the specific needs of different fruit varieties and exploring the applicability of machine vision in real-world scenarios, such as fruit packaging and logistics. This review underscores the critical role of machine vision in non-destructive fruit classification and grading, with numerous opportunities for further research and innovation. As the agricultural industry continues to evolve, integrating machine vision technologies will be instrumental in improving fruit quality assessment, reducing food waste, and enhancing the overall efficiency of fruit processing and distribution
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