A Comprehensive Review on Intelligent Techniques in Crop Pests and Diseases

Artificial intelligence (AI) has transformative potential in the agricultural sector, particularly in managing and preventing crop diseases and pest infestations. This review discusses the significance of early detection and precise diagnosis of various AI tools and techniques for disease identification, such as image processing, machine learning, and deep learning. It also addresses the challenges of AI implementation in agriculture, including data quality, costs, and ethical concerns. The analysis classifies the hurdles and AI offers benefits such as improved resource management, timely interventions, and enhanced productivity. Collaborative efforts are essential to harness AI's potential for sustainable and resilient agriculture

Automated Quality Control in Manufacturing Production Lines: A Robust Technique to Perform Product Quality Inspection

Quality control (QC) in manufacturing processes is critical to ensuring consumers receive products with proper functionality and reliability. Faulty products can lead to additional costs for the manufacturer and damage trust in a brand. A growing trend in QC is the use of machine vision (MV) systems because of their noncontact inspection, high repeatability, and efficiency. This thesis presents a robust MV system developed to perform comparative dimensional inspection on diversely shaped samples. Perimeter, area, rectangularity, and circularity are determined in the dimensional inspection algorithm for a base item and test items. A score determined with the four obtained parameter values provides the likeness between the base item and a test item. Additionally, a surface defect inspection is offered capable of identifying scratches, dents, and markings. The dimensional and surface inspections are used in a QC industrial case study. The case study examines the existing QC system for an electric motor manufacturer and proposes the developed QC system to increase product inspection count and efficiency while maintaining accuracy and reliability. Finally, the QC system is integrated in a simulated product inspection line consisting of a robotic arm and conveyor belts. The simulated product inspection line could identify the correct defect in all tested items and demonstrated the system’s automation capabilities

Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: a review

A Quick and precise crop leaf disease detection is important to increasing agricultural yield in a sustainable manner. We present a comprehensive overview of recent research in the field of crop leaf disease prediction using image processing (IP), machine learning (ML) and deep learning (DL) techniques in this paper. Using these techniques, crop leaf disease prediction made it possible to get notable accuracies. This article presents a survey of research papers that presented the various methodologies, analyzes them in terms of the dataset, number of images, number of classes, algorithms used, convolutional neural networks (CNN) models employed, and overall performance achieved. Then, suggestions are prepared on the most appropriate algorithms to deploy in standard, mobile/embedded systems, Drones, Robots and unmanned aerial vehicles (UAV). We discussed the performance measures used and listed some of the limitations and future works that requires to be focus on, to extend real time automated crop leaf disease detection system

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

Computer-based detection and classification of flaws in citrus fruits

[EN] In this paper, a system for quality control in citrus fruits is presented. In current citrus manufacturing industries, calliper and color are successfully used for the automatic classification of fruits using vision systems. However, the detection of flaws in the citrus surface is carried out by means of human inspection. In this work, a computer vision system capable of detecting defects in the citrus peel and also classifying the type of flaw is presented. First, a review of citrus illnesses has been carried out in order to build a database of digitalized oranges classified by the kind of fault, which is used as a training set. The segmentation of faulty zones is performed by applying the Sobel gradient to the image. Afterwards, color and texture features of the flaw are extracted considering different color spaces, some of them related to high order statistics. Several techniques have been employed for classification purposes: Euler distance to a prototype, to the nearest neighbor and k-nearest neighbors. Additionally, a three layer neural network has been tested and compared, obtaining promising results.López Monfort, JJ.; Cobos Serrano, M.; Aguilera Martí, E. (2011). Computer-based detection and classification of flaws in citrus fruits. Neural Computing and Applications. 20(7):975-981. doi:10.1007/s00521-010-0396-2S975981207Blasco J, Aleixos J, Molto E (2007) Computer vision detection of peel defects in citrus by means of a region oriented segmentation. J Food Eng 81:535–543Blasco J, Aleixos N, Gomez J, Molto E (2007) Citrus sorting by identification of the most common defects using multispectral computer vision. J Food Eng 83:384–391Bryson AE, Ho YC (1969) Applied optimal control: optimization, estimation, and control. Xerox College Publishing, Lexington, MAConners RWea (1983) Identifying and locating surface defects in wood. IEEE Trans Pattern Anal Mach Intell 5:573–583Diaz R, Gil L, Serrano C, Blasco M, Molto E, Blasco J (2004) Comparison of three algorithms in the classification of table olives by means of computer vision. J Food Eng 61:101–107Douglas DH, Peucker TK (1973) Algorithm for the reduction of the number of points required to represent a line or its caricature. The Can Cartogr 10(2):112–122Du CJ, Sun DW (2005) Comparison of three methods for classification of pizza topping using different colour space transformations. J Food Eng 68:277–287Kolesnikov A (2003) Efficient algorithms for vectorization and polygonal approximation. Ph.D. thesis, University of Joensuu, FinlandMolto E (1997) A computer vision system for inspecting citrus, peaches and apples. In: Proceedings of VII national symposium on pattern recognition and image analysis. Sabadell, Spain, pp 121–126Muir AY, Porteus RL, Wastie RL (1982) Experiments in the detection of incipient diseases in potato tubers by optical methods. J Agric Eng Res 27:131–138Q Li (2002) Computer vision based system for apple surface defect detection. computer and electronics in agriculture. Comput Electron Agric 36:215–223Ruiz LA, Molto E, Juste F, Pla F, Valiente R (1996) Location and characterization of the stem–calyx area on oranges by computer vision. J Agric Eng Res 64:165–172Tan TSC, Kittler J (1994) Colour texture analysis using colour histogram. IEEE Proc Vis Image Signal Process 141:403–412Wen Z, Tao Y (1999) Building a rule-based machine-vision system for defect inspection on apple sorting and packing lines. Expert Syst Appl 16:307–31

Computer vision based classification of fruits and vegetables for self-checkout at supermarkets

The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide range of areas such as the development of autonomous vehicles, object recognition, Content Based Image Retrieval (CBIR), image segmentation and terrestrial analysis from space (i.e. crop estimation). Despite significant prior research, the area of object recognition still has many topics to be explored. This PhD thesis focuses on using advanced machine learning approaches to enable the automated recognition of fresh produce (i.e. fruits and vegetables) at supermarket self-checkouts. This type of complex classification task is one of the most recently emerging applications of advanced computer vision approaches and is a productive research topic in this field due to the limited means of representing the features and machine learning techniques for classification. Fruits and vegetables offer significant inter and intra class variance in weight, shape, size, colour and texture which makes the classification challenging. The applications of effective fruit and vegetable classification have significant importance in daily life e.g. crop estimation, fruit classification, robotic harvesting, fruit quality assessment, etc. One potential application for this fruit and vegetable classification capability is for supermarket self-checkouts. Increasingly, supermarkets are introducing self-checkouts in stores to make the checkout process easier and faster. However, there are a number of challenges with this as all goods cannot readily be sold with packaging and barcodes, for instance loose fresh items (e.g. fruits and vegetables). Adding barcodes to these types of items individually is impractical and pre-packaging limits the freedom of choice when selecting fruits and vegetables and creates additional waste, hence reducing customer satisfaction. The current situation, which relies on customers correctly identifying produce themselves leaves open the potential for incorrect billing either due to inadvertent error, or due to intentional fraudulent misclassification resulting in financial losses for the store. To address this identified problem, the main goals of this PhD work are: (a) exploring the types of visual and non-visual sensors that could be incorporated into a self-checkout system for classification of fruits and vegetables, (b) determining a suitable feature representation method for fresh produce items available at supermarkets, (c) identifying optimal machine learning techniques for classification within this context and (d) evaluating our work relative to the state-of-the-art object classification results presented in the literature. An in-depth analysis of related computer vision literature and techniques is performed to identify and implement the possible solutions. A progressive process distribution approach is used for this project where the task of computer vision based fruit and vegetables classification is divided into pre-processing and classification techniques. Different classification techniques have been implemented and evaluated as possible solution for this problem. Both visual and non-visual features of fruit and vegetables are exploited to perform the classification. Novel classification techniques have been carefully developed to deal with the complex and highly variant physical features of fruit and vegetables while taking advantages of both visual and non-visual features. The capability of classification techniques is tested in individual and ensemble manner to achieved the higher effectiveness. Significant results have been obtained where it can be concluded that the fruit and vegetables classification is complex task with many challenges involved. It is also observed that a larger dataset can better comprehend the complex variant features of fruit and vegetables. Complex multidimensional features can be extracted from the larger datasets to generalise on higher number of classes. However, development of a larger multiclass dataset is an expensive and time consuming process. The effectiveness of classification techniques can be significantly improved by subtracting the background occlusions and complexities. It is also worth mentioning that ensemble of simple and less complicated classification techniques can achieve effective results even if applied to less number of features for smaller number of classes. The combination of visual and nonvisual features can reduce the struggle of a classification technique to deal with higher number of classes with similar physical features. Classification of fruit and vegetables with similar physical features (i.e. colour and texture) needs careful estimation and hyper-dimensional embedding of visual features. Implementing rigorous classification penalties as loss function can achieve this goal at the cost of time and computational requirements. There is a significant need to develop larger datasets for different fruit and vegetables related computer vision applications. Considering more sophisticated loss function penalties and discriminative hyper-dimensional features embedding techniques can significantly improve the effectiveness of the classification techniques for the fruit and vegetables applications

A comparative analysis of machine learning approaches for plant disease identification

Background: The problems to leaf in plants are very severe and they usually shorten the lifespan of plants. Leaf diseases are mainly caused due to three types of attacks including viral, bacterial or fungal. Diseased leaves reduce the crop production and affect the agricultural economy. Since agriculture plays a vital role in the economy, thus effective mechanism is required to detect the problem in early stages.Methods: Traditional approaches used for the identification of diseased plants are based on field visits which is time consuming and tedious. In this paper a comparative analysis of machine learning approaches has been presented for the identification of healthy and non-healthy plant leaves. For experimental purpose three different types of plant leaves have been selected namely, cabbage, citrus and sorghum. In order to classify healthy and non-healthy plant leaves color based features such as pixels, statistical features such as mean, standard deviation, min, max and descriptors such as Histogram of Oriented Gradients (HOG) have been used.Results:  382 images of cabbage, 539 images of citrus and 262 images of sorghum were used as the primary dataset. The 40% data was utilized for testing and 60% were used for training which consisted of both healthy and damaged leaves. The results showed that random forest classifier is the best machine method for classification of healthy and diseased plant leaves.Conclusion:  From the extensive experimentation it is concluded that features such as color information, statistical distribution and histogram of gradients provides sufficient clue for the classification of healthy and non-healthy plants

Elección de características de interés en la clasificación de granos de café mediante un sistema de visión por computadora

RESUMEN La clasificación de la calidad del café antes de ser tostado, una de las operaciones más importantes para definir su calidad y precio en el mercado, se realiza manualmente por personal entrenado en el reconocimiento de los defectos del café. Sin embargo, el carácter subjetivo, costo y tiempo que este involucra genera un campo de investigación importante para la aplicación de tecnologías como la visión artificial. El objetivo de este estudio fue evaluar la capacidad de identificación de defectos y clasificación de granos de café mediante un sistema de visión por computadora en el espacio red-green- blue (RGB). Para este fin se implementó un sistema de adquisición y análisis de imágenes, desarrollando una aplicación informática en Matlab 2015ª. Se compraron en el mercado local muestras de café verde, clasificando cada grano de acuerdo con la NTP 209.027 2001. Se adquirieron las imágenes de cada clase y se analizaron determinando en cada grano seis parámetros de forma, seis parámetros de color, en los espacios RGB y HSV, y dos índices o diferencias normalizadas. Se determinaron los parámetros con influencia estadística en la clasificación mediante software de análisis de datos WEKA y se implementaron tres modelos de clasificación máquinas de soporte vectorial (Support Vector Machine - SVM), arboles de decisión (Decision Tree - DT) y K-vecino más cercano (K-Nearest Neighbor). Los tres tipos de clasificador utilizados en la presente investigación mostraron precisión entre 89% y 92.3% lo cual prueba la posibilidad de implementar sistemas basados en imágenes RGB para clasificar granos de café.ABSTRACT The classification of quality on coffee before toasting, one of the most important operations to define its quality and price in the market, is done manually by personnel trained in the recognition of coffee defects. However, the subjective nature, the cost and the time that it involves generates an important research field for the application of technologies such as artificial vision. The objective of this study was to evaluate the ability to identify defects and classify coffee beans using a computer vision system in the red-green-blue (RGB) space. For this purpose, a system for acquisition and analysis of images was implemented, developing a computer application in Matlab 2015ª. Samples of green coffee were purchased on local market, each grain being classified according to NTP 209.027 2001. Images of each class were acquired and analyzed by determining in each grain six shape parameters, six color parameters, in the RGB and HSV spaces, and two normalized indices or differences. Statistical relevance of parameters was deterined using the software for data analysis named WEKA and using these three models of classification, vector machines (SVM), decision trees and nearest K-neighbor (K-neighbor), were implemented. The three types of classifier used in the present investigation show accuracy between 89% and 92.3% which probe the possibility to implement systems based on RGB image to classify coffee been

Image Analysis and Machine Learning in Agricultural Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed. Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could help with agricultural data collection. In the first chapter, information related to different types of imaging (e.g., RGB, multi/hyperspectral, and thermal imaging) was explored in detail for its advantages in different agriculture applications. The process of image analysis demonstrated how target features were extracted for analysis including shape, edge, texture, and color. After acquiring features information, machine learning can be used to automatically detect or predict features of interest such as disease severity. In the second chapter, case studies of different agricultural applications were demonstrated including: 1) leaf damage symptoms, 2) stress evaluation, 3) plant growth evaluation, 4) stand/insect counting, and 5) evaluation for produce quality. Case studies showed that the use of image analysis is often more advantageous than visual rating. Advantages of image analysis include increased objectivity, speed, and more reproducibly reliable results. In the third chapter, machine learning was explored using romaine lettuce images from RD4AG to automatically grade for bolting and compactness (two of the important parameters for lettuce quality). Although the accuracy is at 68.4 and 66.6% respectively, a much larger data base and many improvements are needed to increase the model accuracy and reliability. With the advancement in cameras, computers with high computing power, and the development of different algorithms, image analysis and machine learning have the potential to replace part of the labor and improve the current data collection procedure in agricultural research. Advisor: Gary L. Hei

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure