Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy

It is widely known that the visible and near infrared (VIS-NIR) spectroscopy has the potential of estimating soil total nitrogen (TN), organic carbon (OC) and moisture content (MC) due to the direct spectral responses these properties have in the near infrared (NIR) region. However, improving the prediction accuracy requires advanced modelling techniques, particularly when measurement is planned for fresh (wet and un-processed) soil samples. The aim of this work is to compare the predictive performance of two linear multivariate and two machine learning methods for TN, OC and MC. The two multivariate methods investigated included principal component regression (PCR) and partial least squares regression (PLSR), whereas the machine learning methods included least squares support vector machines (LS-SVM), and Cubist. A mobile, fibre type, VIS-NIR spectrophotometer was utilised to collect soil spectra (305–2200 nm) in diffuse reflectance mode from 140 wet soil samples collected from one field in Germany. The results indicate that machine learning methods are capable of tackling non-linear problems in the dataset. LS-SVMs and the Cubist method out-performed the linear multivariate methods for the prediction of all three soil properties studied. LS-SVM provided the best prediction for MC (root mean square error of prediction (RMSEP) = 0.457% and residual prediction deviation (RPD) = 2.24) and OC (RMSEP = 0.062% and RPD = 2.20), whereas the Cubist method provided the best prediction for TN (RMSEP = 0.071 and RPD = 1.96)

Development of computational intelligence and data fusion methods in biosystems engineering

The main objective of the presented thesis is to develop methods of Computational Intelligence and Data Fusion with application in the Biosystems Engineering area for the non-destructive testing of agricultural products and crop condition monitoring. The developed methods are related to the combination of sensors with Artificial Intelligence architectures in Precision Agriculture. The developed Artificial Intelligence algorithms included Bio-inspired Hierarchical Neural Maps and Novelty Detection algorithms that are capable of detecting sudden changes in different conditions of Biosystems Engineering area. In this thesis, Hierarchical models of Self-Organizing Maps (SOMs) in two different applications of Biosystems Engineering area are presented which included wheat yield prediction by data fusion from ground sensors and satellite images and harvesting stage detection in lettuce plants. Moreover, it is already known that in many cases of Biosystems Engineering applications, the condition of crops is not already known through Data Mining approaches. For this reason, it is necessary to determine their position by intelligent sensors which are capable of detecting whether any features are already memorized by a learning structure or an entirely new situation. In this thesis, Novelty Detection algorithms were utilized for the detection of new situations such as: i) weed species detection based on spectral characteristics, ii) Septoria and water stress detection based on spectral data and yellow rust, and iii) nitrogen stress detection based on spectral characteristics. Finally, this thesis presents an intelligent multi-disease plant identification system which was developed by utilizing leaf images. This smart system used the Support Vector Machine entsemple compared to the Active Learning approximation for plant diseases identification. By this method it is concluded that the first approach is particularly based on Support Vector Machines and the Active Learning approach is capable of using any One Class Classifier. The proposed methods were able to overcome all the above problems by presenting an over 90 % high yield depending on the application. The presented methods have managed to solve several key problems that are related to Precision Agriculture and act as useful tools for future use in larger scale practices.Βασικός στόχος της παρούσας διδακτορικής διατριβής, είναι η ανάπτυξη μεθόδων Υπολογιστικής Νοημοσύνης και Σύντηξης Δεδομένων με εφαρμογή στο πεδίο της Μηχανικής Βιοσυστημάτων για το μη καταστροφικό έλεγχο αγροτικών προϊόντων καθώς και την παρακολούθηση της κατάστασης των καλλιεργειών. Οι μέθοδοι που αναπτύχθηκαν αφορούν το συνδυασμό αισθητήρων στη Γεωργία Ακριβείας με Τεχνητή Νοημοσύνη. Οι αλγόριθμοι Τεχνητής Νοημοσύνης που αναπτύχθηκαν περιλαμβάνουν Βιο-εμπνευσμένους Ιεραρχικούς Νευρωνικούς Χάρτες καθώς και αλγόριθμους Ανίχνευσης Καινοτομίας που ανιχνεύουν απότομες αλλαγές καταστάσεων το πεδίο της Μηχανικής Βιοσυστημάτων. Στην παρούσα διατριβή παρουσιάστηκαν ιεραρχικά μοντέλα Αυτό-Οργανούμενων Χαρτών σε δυο διαφορετικές εφαρμογές της Μηχανικής Βιοσυστημάτων οι οποίες περιελάμβαναν :την πρόβλεψη απόδοσης μιας καλλιέργειας σιταριού με βάση τη σύντηξη η οποία πραγματοποιείται από αισθητήρες εδάφους και εικόνες οι οποίες προέρχονται από δορυφόρο και την ανίχνευση του σταδίου συγκομιδής σε φυτά καλλιέργειας μαρουλιού. Επιπλέον, είναι ήδη γνωστό ότι σε πολλές εφαρμογές της Μηχανικής Βιοσυστημάτων, η κατάσταση των καλλιεργειών δεν είναι γνωστή εκ των προτέρων μέσω της Εξόρυξης Δεδομένων. Για το λόγο αυτό κρίνεται απαραίτητος ο προσδιορισμός της κατάσταση τους με τη βοήθεια έξυπνων αισθητήρων οι οποίοι μπορούν να ανιχνεύσουν εάν κάποια χαρακτηριστικά έχουν ήδη απομνημονευτεί από μια δομή εκμάθησης ή αποτελούν εξ’ολοκλήρου μια νέα κατάσταση. Για την ανίχνευση νέων καταστάσεων χρησιμοποιήθηκαν στην παρούσα διατριβή Αλγόριθμοι Ανίχνευσης Καινοτομίας σε διαφορετικές καταστάσεις όπως: την ανίχνευση ειδών ζιζανίων με βάση φασματικά χαρακτηριστικά, την ανίχνευση Σεπτόριας και υδατικής καταπόνησης με βάση φασματικά χαρακτηριστικά καθώς και την ανίχνευση Κίτρινης Σκωρίασης και καταπόνησης λόγω ελλείψεως αζώτου με βάση φασματικά χαρακτηριστικά. Τέλος, στην παρούσα διατριβή παρουσιάζεται ένα έξυπνο σύστημα αναγνώρισης πολλαπλών ασθενειών σε φυτά το οποίο αναπτύχθηκε κάνοντας χρήση εικόνων των φύλλων τους. Το συγκεκριμένο έξυπνο σύστημα χρησιμοποιώντας το σμήνος των Μηχανών Διανυσματικής Υποστήριξης σε σχέση με την προσέγγιση της Ενεργούς Εκμάθησης για την αναγνώριση ασθενειών σε φύλλα φυτών, ήταν δυνατό να εξάγει το συμπέρασμα ότι η πρώτη προσέγγιση είναι ιδιαιτέρως βασισμένη στις Μηχανές Διανυσματικής Υποστήριξης ενώ η προσέγγιση της Ενεργούς Εκμάθησης μπορεί να χρησιμοποιήσει κάθε Ταξινομητή μιας Τάξης. Οι προτεινόμενες μέθοδοι ήταν δυνατόν να επιλύσουν όλα τα παραπάνω προβλήματα παρουσιάζοντας υψηλή απόδοση της τάξεως άνω του 90% ανάλογα με την εφαρμογή. Με τη βοήθεια των μεθόδων αυτών, επιλύονται πολλαπλά βασικά προβλήματα που αφορούν τη Γεωργία Ακριβείας και επιπλέον τίθενται οι βάσεις για μελλοντική εκμετάλλευση των πρακτικών αυτών σε μεγαλύτερη κλίμακα

Intelligent Data Mining and Fusion Systems in Agriculture

ntelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion that have applications in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. Sections cover the combination of sensors with artificial intelligence architectures in precision agriculture, including algorithms, bio-inspired hierarchical neural maps, and novelty detection algorithms capable of detecting sudden changes in different conditions. This book offers advanced students and entry-level professionals in agricultural science and engineering, geography and geoinformation science an in-depth overview of the connection between decision-making in agricultural operations and the decision support features offered by advanced computational intelligence algorithms

Comparison of Deep Neural Networks in Detecting Field Grapevine Diseases Using Transfer Learning

Plants diseases constitute a substantial threat for farmers given the high economic and environmental impact of their treatment. Detecting possible pathogen threats in plants based on non-destructive remote sensing and computer vision methods offers an alternative to existing laboratory methods and leads to improved crop management. Vine is an important crop that is mainly affected by fungal diseases. In this study, photos from healthy leaves and leaves infected by a fungal disease of vine are used to create disease identification classifiers. The transfer learning technique was employed in this study and was used to train three different deep convolutional neural network (DCNN) approaches that were compared according to their classification accuracy, namely AlexNet, VGG-19, and Inception v3. The above-mentioned models were trained on the open-source PlantVillage dataset using two training approaches: feature extraction, where the weights of the base deep neural network model were frozen and only the ones on the newly added layers were updated, and fine tuning, where the weights of the base model were also updated during training. Then, the created models were validated on the PlantVillage dataset and retrained using a custom field-grown vine photo dataset. The results showed that the fine-tuning approach showed better validation and testing accuracy, for all DCNNs, compared to the feature extraction approach. As far as the results of DCNNs are concerned, the Inception v3 algorithm outperformed VGG-19 and AlexNet in almost all the cases, demonstrating a validation performance of 100% for the fine-tuned strategy on the PlantVillage dataset and an accuracy of 83.3% for the respective strategy on a custom vine disease use case dataset, while AlexNet achieved 87.5% validation and 66.7% accuracy for the respective scenarios. Regarding VGG-19, the validation performance reached 100%, with an accuracy of 76.7%

Comparison of Deep Neural Networks in Detecting Field Grapevine Diseases Using Transfer Learning

Plants diseases constitute a substantial threat for farmers given the high economic and environmental impact of their treatment. Detecting possible pathogen threats in plants based on non-destructive remote sensing and computer vision methods offers an alternative to existing laboratory methods and leads to improved crop management. Vine is an important crop that is mainly affected by fungal diseases. In this study, photos from healthy leaves and leaves infected by a fungal disease of vine are used to create disease identification classifiers. The transfer learning technique was employed in this study and was used to train three different deep convolutional neural network (DCNN) approaches that were compared according to their classification accuracy, namely AlexNet, VGG-19, and Inception v3. The above-mentioned models were trained on the open-source PlantVillage dataset using two training approaches: feature extraction, where the weights of the base deep neural network model were frozen and only the ones on the newly added layers were updated, and fine tuning, where the weights of the base model were also updated during training. Then, the created models were validated on the PlantVillage dataset and retrained using a custom field-grown vine photo dataset. The results showed that the fine-tuning approach showed better validation and testing accuracy, for all DCNNs, compared to the feature extraction approach. As far as the results of DCNNs are concerned, the Inception v3 algorithm outperformed VGG-19 and AlexNet in almost all the cases, demonstrating a validation performance of 100% for the fine-tuned strategy on the PlantVillage dataset and an accuracy of 83.3% for the respective strategy on a custom vine disease use case dataset, while AlexNet achieved 87.5% validation and 66.7% accuracy for the respective scenarios. Regarding VGG-19, the validation performance reached 100%, with an accuracy of 76.7%

Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination

Microbotryum silybum, a smut fungus, is studied as an agent for the biological control of Silybum marianum (milk thistle) weed. Confirmation of the systemic infection is essential in order to assess the effectiveness of the biological control application and assist decision-making. Nonetheless, in situ diagnosis is challenging. The presently demonstrated research illustrates the identification process of systemically infected S. marianum plants by means of field spectroscopy and the multilayer perceptron/automatic relevance determination (MLP-ARD) network. Leaf spectral signatures were obtained from both healthy and infected S. marianum plants using a portable visible and near-infrared spectrometer (310–1100 nm). The MLP-ARD algorithm was applied for the recognition of the infected S. marianum plants. Pre-processed spectral signatures served as input features. The spectra pre-processing consisted of normalization, and second derivative and principal component extraction. MLP-ARD reached a high overall accuracy (90.32%) in the identification process. The research results establish the capacity of MLP-ARD to precisely identify systemically infected S. marianum weeds during their vegetative growth stage

Non-Destructive Quality Estimation Using a Machine Learning-Based Spectroscopic Approach in Kiwifruits

The current study investigates the use of a non-destructive hyperspectral imaging approach for the evaluation of kiwifruit cv. “Hayward” internal quality, focusing on physiological traits such as soluble solid concentration (SSC), dry matter (DM), firmness, and tannins, widely used as quality attributes. Regression models, including partial least squares regression (PLSR), bagged trees (BTs), and three-layered neural network (TLNN), were employed for the estimation of the above-mentioned quality attributes. Experimental procedures involving the Specim IQ hyperspectral camera utilization and software were followed for data acquisition and analysis. The effectiveness of PLSR, bagged trees, and TLNN in predicting the firmness, SSC, DM, and tannins of kiwifruit was assessed via statistical metrics, including R squared (R²) values and the root mean square error (RMSE). The obtained results indicate varying degrees of efficiency for each model in predicting kiwifruit quality parameters. The study concludes that machine learning algorithms, especially neural networks, offer substantial accuracy, surpassing traditional methods for evaluating kiwifruit quality traits. Overall, the current study highlights the potential of such non-destructive techniques in revolutionizing quality assessment during postharvest by yielding rapid and reliable predictions regarding the critical quality attributes of fruits

Non-Destructive Early Detection and Quantitative Severity Stage Classification of Tomato Chlorosis Virus (ToCV) Infection in Young Tomato Plants Using Vis–NIR Spectroscopy

Tomato chlorosis virus (ToCV) is a serious, emerging tomato pathogen that has a significant impact on the quality and quantity of tomato production worldwide. Detecting ToCV via means of spectral measurements in an early pre-symptomatic stage offers an alternative to the existing laboratory methods, leading to better disease management in the field. In this study, leaf spectra from healthy and diseased leaves were measured with a spectrometer. The diseased leaves were subjected to RT-qPCR for the detection and quantification of the titer of ToCV. Neighborhood component analysis (NCA) algorithm was employed for the feature selection of the effective wavelengths and the most important vegetation indices out of the 24 that were tested. Two machine learning methods, namely XY-fusion network (XY-F) and multilayer perceptron with automated relevance determination (MLP–ARD), were employed for the estimation of the disease existence and viral load in the tomato leaves. The results showed that before outlier elimination, the MLP–ARD classifier generally outperformed the XY-F network with an overall accuracy of 92.1% against 88.3% for the XY-F. Outlier elimination contributed to the performance of the classifiers as the overall accuracy for both XY-F and MLP–ARD reached 100%

Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images

In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery