IMPLEMENTATION OF GENETIC ALGORITHM BASED ARTIFICIAL NEURAL NETWORK TO IDENTIFY VEGETABLES WITH PHYSIOLOGICAL DISEASES

We synthetically applied computer vision, genetic algorithm and artificial neural network technology to automatically identify the vegetables (tomatoes) that had physiological diseases. Initially tomatoes’ images were captured through a computer vision system. Then to identify cavernous tomatoes, we analyzed the roundness and detected deformed tomatoes by applying the variation of vegetable’s diameter. Later, we used a Genetic Algorithm (GA) based artificial neural network (ANN). Experiments show that the above methods can accurately identify vegetables’ shapes and meet requests of classification; the accuracy rate for the identification for vegetables with physiological diseases was up to 100%. [Nature and Science. 2005; 3(2):52-58]

A review of neural networks in plant disease detection using hyperspectral data

© 2018 China Agricultural University This paper reviews advanced Neural Network (NN) techniques available to process hyperspectral data, with a special emphasis on plant disease detection. Firstly, we provide a review on NN mechanism, types, models, and classifiers that use different algorithms to process hyperspectral data. Then we highlight the current state of imaging and non-imaging hyperspectral data for early disease detection. The hybridization of NN-hyperspectral approach has emerged as a powerful tool for disease detection and diagnosis. Spectral Disease Index (SDI) is the ratio of different spectral bands of pure disease spectra. Subsequently, we introduce NN techniques for rapid development of SDI. We also highlight current challenges and future trends of hyperspectral data

Intelligent feature selection for neural regression : techniques and applications

Feature Selection (FS) and regression are two important technique categories in Data Mining (DM). In general, DM refers to the analysis of observational datasets to extract useful information and to summarise the data so that it can be more understandable and be used more efficiently in terms of storage and processing. FS is the technique of selecting a subset of features that are relevant to the development of learning models. Regression is the process of modelling and identifying the possible relationships between groups of features (variables). Comparing with the conventional techniques, Intelligent System Techniques (ISTs) are usually favourable due to their flexible capabilities for handling real‐life problems and the tolerance to data imprecision, uncertainty, partial truth, etc. This thesis introduces a novel hybrid intelligent technique, namely Sensitive Genetic Neural Optimisation (SGNO), which is capable of reducing the dimensionality of a dataset by identifying the most important group of features. The capability of SGNO is evaluated with four practical applications in three research areas, including plant science, civil engineering and economics. SGNO is constructed using three key techniques, known as the core modules, including Genetic Algorithm (GA), Neural Network (NN) and Sensitivity Analysis (SA). The GA module controls the progress of the algorithm and employs the NN module as its fitness function. The SA module quantifies the importance of each available variable using the results generated in the GA module. The global sensitivity scores of the variables are used determine the importance of the variables. Variables of higher sensitivity scores are considered to be more important than the variables with lower sensitivity scores. After determining the variables’ importance, the performance of SGNO is evaluated using the NN module that takes various numbers of variables with the highest global sensitivity scores as the inputs. In addition, the symbolic relationship between a group of variables with the highest global sensitivity scores and the model output is discovered using the Multiple‐Branch Encoded Genetic Programming (MBE‐GP). A total of four datasets have been used to evaluate the performance of SGNO. These datasets involve the prediction of short‐term greenhouse tomato yield, prediction of longitudinal dispersion coefficients in natural rivers, prediction of wave overtopping at coastal structures and the modelling of relationship between the growth of industrial inputs and the growth of the gross industrial output. SGNO was applied to all these datasets to explore its effectiveness of reducing the dimensionality of the datasets. The performance of SGNO is benchmarked with four dimensionality reduction techniques, including Backward Feature Selection (BFS), Forward Feature Selection (FFS), Principal Component Analysis (PCA) and Genetic Neural Mathematical Method (GNMM). The applications of SGNO on these datasets showed that SGNO is capable of identifying the most important feature groups of in the datasets effectively and the general performance of SGNO is better than those benchmarking techniques. Furthermore, the symbolic relationships discovered using MBE‐GP can generate performance competitive to the performance of NN models in terms of regression accuracies

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

딥러닝 방법론을 이용한 높은 적용성을 가진 수경재배 파프리카 대상 절차 기반 모델 개발

학위논문(박사) -- 서울대학교대학원 : 농업생명과학대학 농림생물자원학부, 2022. 8. 손정익.Many agricultural challenges are entangled in a complex interaction between crops and the environment. As a simplifying tool, crop modeling is a process of abstracting and interpreting agricultural phenomena. Understanding based on this interpretation can play a role in supporting academic and social decisions in agriculture. Process-based crop models have solved the challenges for decades to enhance the productivity and quality of crop production; the remaining objectives have led to demand for crop models handling multidirectional analyses with multidimensional information. As a possible milestone to satisfy this goal, deep learning algorithms have been introduced to the complicated tasks in agriculture. However, the algorithms could not replace existing crop models because of the research fragmentation and low accessibility of the crop models. This study established a developmental protocol for a process-based crop model with deep learning methodology. Literature Review introduced deep learning and crop modeling, and it explained the reasons for the necessity of this protocol despite numerous deep learning applications for agriculture. Base studies were conducted with several greenhouse data in Chapters 1 and 2: transfer learning and U-Net structure were utilized to construct an infrastructure for the deep learning application; HyperOpt, a Bayesian optimization method, was tested to calibrate crop models to compare the existing crop models with the developed model. Finally, the process-based crop model with full deep neural networks, DeepCrop, was developed with an attention mechanism and multitask decoders for hydroponic sweet peppers (Capsicum annuum var. annuum) in Chapter 3. The methodology for data integrity showed adequate accuracy, so it was applied to the data in all chapters. HyperOpt was able to calibrate food and feed crop models for sweet peppers. Therefore, the compared models in the final chapter were optimized using HyperOpt. DeepCrop was trained to simulate several growth factors with environment data. The trained DeepCrop was evaluated with unseen data, and it showed the highest modeling efficiency (=0.76) and the lowest normalized root mean squared error (=0.18) than the compared models. With the high adaptability of DeepCrop, it can be used for studies on various scales and purposes. Since all methods adequately solved the given tasks and underlay the DeepCrop development, the established protocol can be a high throughput for enhancing accessibility of crop models, resulting in unifying crop modeling studies.농업 시스템에서 발생하는 문제들은 작물과 환경의 상호작용 하에 복잡하게 얽혀 있다. 작물 모델링은 대상을 단순화하는 방법으로써, 농업에서 일어나는 현상을 추상화하고 해석하는 과정이다. 모델링을 통해 대상을 이해하는 것은 농업 분야의 학술적 및 사회적 결정을 지원할 수 있다. 지난 수년 간 절차 기반 작물 모델은 농업의 문제들을 해결하여 작물 생산성 및 품질을 증진시켰으며, 현재 작물 모델링에 남아있는 과제들은 다차원 정보를 다방향에서 분석할 수 있는 작물 모델을 필요로 하게 되었다. 이를 만족시킬 수 있는 지침으로써, 복잡한 농업적 과제들을 목표로 딥러닝 알고리즘이 도입되었다. 그러나, 이 알고리즘들은 낮은 데이터 완결성 및 높은 연구 다양성 때문에 기존의 작물 모델들을 대체하지는 못했다. 본 연구에서는 딥러닝 방법론을 이용하여 절차 기반 작물 모델을 구축하는 개발 프로토콜을 확립하였다. Literature Review에서는 딥러닝과 작물 모델에 대해 소개하고, 농업으로의 딥러닝 적용 연구가 많음에도 이 프로토콜이 필요한 이유를 설명하였다. 제1장과 2장에서는 국내 여러 지역의 데이터를 이용하여 전이 학습 및 U-Net 구조를 활용하여 딥러닝 모델 적용을 위한 기반을 마련하고, 베이지안 최적화 방법인 HyperOpt를 사용하여 기존 모델과 딥러닝 기반 모델을 비교하기 위해 시험적으로 WOFOST 작물 모델을 보정하는 등 모델 개발을 위한 기반 연구를 수행하였다. 마지막으로, 제3장에서는 주의 메커니즘 및 다중 작업 디코더를 가진 완전 심층 신경망 절차 기반 작물 모델인 DeepCrop을 수경재배 파프리카(Capsicum annuum var. annuum) 대상으로 개발하였다. 데이터 완결성을 위한 기술들은 적합한 정확도를 보여주었으며, 전체 챕터 데이터에 적용하였다. HyperOpt는 식량 및 사료 작물 모델들을 파프리카 대상으로 보정할 수 있었다. 따라서, 제3장의 비교 대상 모델들에 대해 HyperOpt를 사용하였다. DeepCrop은 환경 데이터를 이용하고 여러 생육 지표를 예측하도록 학습되었다. 학습에 사용하지 않은 데이터를 이용하여 학습된 DeepCrop를 평가하였으며, 이 때 비교 모델들 중 가장 높은 모형 효율(EF=0.76)과 가장 낮은 표준화 평균 제곱근 오차(NRMSE=0.18)를 보여주었다. DeepCrop은 높은 적용성을 기반으로 다양한 범위와 목적을 가진 연구에 사용될 수 있을 것이다. 모든 방법들이 주어진 작업을 적절히 풀어냈고 DeepCrop 개발의 근거가 되었으므로, 본 논문에서 확립한 프로토콜은 작물 모델의 접근성을 향상시킬 수 있는 획기적인 방향을 제시하였고, 작물 모델 연구의 통합에 기여할 수 있을 것으로 기대한다.LITERATURE REVIEW 1 ABSTRACT 1 BACKGROUND 3 REMARKABLE APPLICABILITY AND ACCESSIBILITY OF DEEP LEARNING 12 DEEP LEARNING APPLICATIONS FOR CROP PRODUCTION 17 THRESHOLDS TO APPLY DEEP LEARNING TO CROP MODELS 18 NECESSITY TO PRIORITIZE DEEP-LEARNING-BASED CROP MODELS 20 REQUIREMENTS OF THE DEEP-LEARNING-BASED CROP MODELS 21 OPENING REMARKS AND THESIS OBJECTIVES 22 LITERATURE CITED 23 Chapter 1 34 Chapter 1-1 35 ABSTRACT 35 INTRODUCTION 37 MATERIALS AND METHODS 40 RESULTS 50 DISCUSSION 59 CONCLUSION 63 LITERATURE CITED 64 Chapter 1-2 71 ABSTRACT 71 INTRODUCTION 73 MATERIALS AND METHODS 75 RESULTS 84 DISCUSSION 92 CONCLUSION 101 LITERATURE CITED 102 Chapter 2 108 ABSTRACT 108 NOMENCLATURE 110 INTRODUCTION 112 MATERIALS AND METHODS 115 RESULTS 124 DISCUSSION 133 CONCLUSION 137 LITERATURE CITED 138 Chapter 3 144 ABSTRACT 144 INTRODUCTION 146 MATERIALS AND METHODS 149 RESULTS 169 DISCUSSION 182 CONCLUSION 187 LITERATURE CITED 188 GENERAL DISCUSSION 196 GENERAL CONCLUSION 201 ABSTRACT IN KOREAN 203 APPENDIX 204박

Finding spectral features for the early identification of biotic stress in plants

Early detection of biotic stress in plants is vital for precision crop protection, but hard to achieve. Prediction of plant diseases or weeds at an early stage has significant influence on the extent and effectiveness of crop protection measures. The precise measure depends on specific weeds and plant diseases and their economic thresholds. Weeds and plant diseases at an early stage, however, are difficult to identify. Non-invasive optical sensors with high resolution are promising for early detection of biotic stress. The data of these sensors, e.g. hyperspectral or fluorescence signatures, contain relevant information about the occurrence of pathogens. Shape parameters, derived from bispectral images, have enormous potential for an early identification of weeds in crops. The analysis of this high dimensional data for an identification of weeds and pathogens as early as possible is demanding as the sensor signal is affected by many influencing factors. Nevertheless, advanced methods of machine learning facilitate the interpretation of these signals. Whereas traditional statistics estimate the posterior probability of the class by probability distribution, machine learning methods provide algorithms for optimising prediction accuracy by the discriminant function. Machine learning methods with robust training algorithms play a key role in handling non-linear classification problems. This thesis presents an approach which integrates modern sensor techniques and advanced machine learning methods for an early detection and differentiation of plant diseases and weeds. Support vector machines (SVMs) equipped with non-linear kernels prove as effective and robust classifiers. Furthermore, it is shown that even a presymptomatic identification based on the combination of spectral vegetation indices is realised. Using well-established data analysis methods of this scientific field, this has not achieved so far. Identifying disease specific features from the underlying original high dimensional sensor data selection is conducted. The high dimensionality of data affords a careful selection of relevant and non-redundant features depending on classification problem and feature properties. In the case of fluorescence signatures an extraction of new features is necessary. In this context modelling of signal noise by an analytical description of the spectral signature improves the accuracy of classification substantially. In the case of weed discrimination accuracy is improved by exploiting the hierarchy of weed species. This thesis outlines the potential of SVMs, feature construction and feature selection for precision crop protection. A problem-specific extraction and selection of relevant features, in combination with task-oriented classification methods, is essential for robust identification of pathogens and weeds as early as possible.Früherkennung von biotischem Pflanzenstress ist für den Präzisionspflanzenschutz wesentlich, aber schwierig zu erreichen. Die Vorhersage von Pflanzenkrankheiten und Unkräutern in einem frühen Entwicklungsstadium hat signifikanten Einfluss auf das Ausmaß und die Effektivität einer Pflanzenschutzmaßnahme. Aufgrund der Abhängigkeit einer Maßnahme von der Art der Pflanzenkrankheit oder des Unkrauts und ihrer ökonomischer Schadschwelle ist eine präzise Identifizierung der Schadursache essentiell, aber gerade im Frühstadium durch die Ähnlichkeit der Schadbilder problematisch. Nicht-invasive optische Sensoren mit hoher Auflösung sind vielversprechend für eine Früherkennung von biotischem Pflanzenstress. Daten dieser Sensoren, beispielsweise Hyperspektral- oder Fluoreszenzspektren, enthalten relevante Informationen über das Auftreten von Pathogenen; Formparameter, abgeleitet aus bispektralen Bildern, zeigen großes Potential für die Früherkennung von Unkräutern in Kulturpflanzen. Die Analyse dieser hochdimensionalen Sensordaten unter Berücksichtigung vielfältiger Faktoren ist eine anspruchsvolle Herausforderung. Moderne Methoden des maschinellen Lernens bieten hier zielführende Möglichkeiten. Während die traditionelle Statistik die a-posteriori Wahrscheinlichkeit der Klasse basierend auf Wahrscheinlichkeitsverteilungen schätzt, verwenden maschinelle Lernverfahren Algorithmen für eine Optimierung der Vorhersagegenauigkeit auf Basis diskriminierender Funktionen. Grundlage zur Bearbeitung dieser nicht-linearen Klassi kationsprobleme sind robuste maschinelle Lernverfahren. Die vorliegende Dissertationsschrift zeigt, dass die Integration moderner Sensortechnik mit fortgeschrittenen Methoden des maschinellen Lernens eine Erkennung und Differenzierung von Pflanzenkrankheiten und Unkräutern ermöglicht. Einen wesentlichen Beitrag für eine effektive und robuste Klassifikation leisten Support Vektor Maschinen (SVMs) mit nicht-linearen Kernels. Weiterhin wird gezeigt, dass SVMs auf Basis spektraler Vegetationsindizes die Detektion von Pflanzenkrankheiten noch vor Auftreten visuell wahrnehmbarer Symptome ermöglichen. Dies wurde mit bekannten Verfahren noch nicht erreicht. Zur Identifikation krankheitsspezifischer Merkmale aus den zugrunde liegenden originären hochdimensionalen Sensordaten wurden Merkmale konstruiert und selektiert. Die Selektion ist sowohl vom Klassifikationsproblem als auch von den Eigenschaften der Merkmale abhängig. Im Fall von Fluoreszenzspektren war eine Extraktion von neuen Merkmalen notwendig. In diesem Zusammenhang trägt die Modellierung des Signalrauschens durch eine analytische Beschreibung der spektralen Signatur zur deutlichen Verbesserung der Klassifikationsgenauigkeit bei. Im Fall der Differenzierung von unterschiedlichen Unkräutern erhöht die Ausnutzung der Hierarchie der Unkrautarten die Genauigkeit signifikant. Diese Arbeit zeigt das Potential von Support Vektor Maschinen, Merkmalskonstruktion und Selektion für den Präzisionspflanzenschutz. Eine problemspezifische Extraktion und Selektion relevanter Merkmale in Verbindung mit sachbezogenen Klassifikationsmethoden ermöglichen eine robuste Identifikation von Pathogenen und Unkräutern zu einem sehr frühen Zeitpunkt

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

IoT in smart communities, technologies and applications.

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing the fundamental components that make up the IoT Smart City landscape, the technologies that enable these domains to exist, the most prevalent practices and techniques which are used in these domains as well as the challenges that deployment of IoT systems for smart cities encounter and which need to be addressed for ubiquitous use of smart city applications. It also presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things. Towards this end, a mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. Within the smart health domain of IoT smart cities, human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. Fall detection is one of the most important tasks in human activity recognition. With an increasingly aging world population and an inclination by the elderly to live alone, the need to incorporate dependable fall detection schemes in smart devices such as phones, watches has gained momentum. Therefore, differentiating between falls and activities of daily living (ADLs) has been the focus of researchers in recent years with very good results. However, one aspect within fall detection that has not been investigated much is direction and severity aware fall detection. Since a fall detection system aims to detect falls in people and notify medical personnel, it could be of added value to health professionals tending to a patient suffering from a fall to know the nature of the accident. In this regard, as a case study for smart health, four different experiments have been conducted for the task of fall detection with direction and severity consideration on two publicly available datasets. These four experiments not only tackle the problem on an increasingly complicated level (the first one considers a fall only scenario and the other two a combined activity of daily living and fall scenario) but also present methodologies which outperform the state of the art techniques as discussed. Lastly, future recommendations have also been provided for researchers

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

Modeling and optimization of environment in agricultural greenhouses for improving cleaner and sustainable crop production

Resource-use efficiency and crop yield are significant factors in the management of agricultural greenhouse. Appropriate modeling methods effectively improve the control performance and efficiency of the greenhouse system and are conducive to the design of water and energy-saving strategies. Meanwhile, the extreme environment could be forecasted in advance, which reduces pests and diseases as well as provides high-quality food. Accordingly, the interest of the scientific community in greenhouse modeling and optimizing has grown considerably. The objective of this work is to provide guidance and insight into the topic by reviewing 73 representative articles and to further support cleaner and sustainable crop production. Compared to the existing literature review, this work details the approaches to improve the greenhouse model in the aspects of parameter identification, structure and process optimization, and multi-model integration to better model complex greenhouse system. Furthermore, a statistical study has been carried out to summarize popular technology and future trends. It was found that dynamic and neural network techniques are most commonly used to establish the greenhouse model and the heuristic algorithm is popular to improve the accuracy and generalization ability of the model. Notably, deep learning, the combination of “knowledge” and “data”, and coupling between the greenhouse system elements have been considered as future valuable development