A Review on Advances in Automated Plant Disease Detection

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images

Improving Breeding Program Efficiency and Genetic Gain Through the Implementation of Genomic Selection in Diverse Wheat Germplasm

Genomic selection (GS) is an important tool for increasing genetic gain for economically important traits in breeding programs. Genomic selection uses molecular markers across the entire genome in order to predict the performance of breeding lines for a trait of interest prior to phenotyping. A training population (TP) of elite germplasm, representative of the University of Arkansas wheat breeding program, was developed in order to predict important agronomic and Fusarium head blight (FHB) resistance traits within the University of Arkansas wheat breeding program through cross-validation and forward prediction. A genome-wide association study (GWAS) was performed on the TP to identify novel FHB resistance loci for deoxynivalenol (DON) accumulation, Fusarium damaged kernels (FDK), incidence (INC), and severity (SEV). Significantly loci were used as fixed effects in a GS model (GS+GWAS) and compared to a naïve GS (NGS) model, where the NGS models had significantly higher prediction accuracies (PA) than the GS+GWAS models for all four FHB traits. The GWAS identified novel loci for all four FHB traits, most notably on chromosomes 3BL and 4BL. Multivariate GS (MVGS) models using correlated traits as covariates were also compared to NGS models and the MVGS models significantly outperformed the NGS models for all four traits. The same TP was also evaluated for five agronomic traits, including grain yield (GY), heading date (HD), maturity date (MD), plant height (PH), and test weight (TW), where MVGS models were compared to NGS models. Again, MVGS models significantly outperformed NGS models for all five agronomic traits, especially when there were strong genetic correlations between predicted traits and covariates. Additionally, MVGS models were tested using GY data for genotypes only grown in some environments to predict said genotypes in missing environments. This method significantly improved PA for GY between 6% and 21% for four of six tested environments. The abovementioned TP was then used for forward prediction to predict GY for untested F4:6 breeding lines and DON, FDK, and SEV for F4:7 breeding lines. The MVGS models were comparable to phenotypic selection and had higher selection accuracies for two of three breeding cycles for GY, both cycles for DON, and at least one cycle for FDK and SEV. The MVGS model also had higher PAs for all four traits compared with the NGS models. These results show that GS, and MVGS, can be successfully implemented in a wheat breeding program over multiple breeding cycles and can be effective alongside phenotypic selection for economically important traits. The MVGS models are particularly effective when predicted traits share strong genetic correlations with covariate traits, and covariate traits have a higher heritability than the predicted traits

Developing affordable high-throughput plant phenotyping methods for breeding of cereals and tuber crops

High-throughput plant phenotyping (HTPP) is a fast, accurate, and non-destructive process for evaluating plants' health and environmental adaptability. HTPP accelerates the identification of agronomic traits of interest, eliminates subjectivism (which is innate to humans), and facilitates the development of adapted genotypes. Current HTPP methods often rely on imaging sensors and computer vision both in the field and under controlled (indoor) conditions. However, their use is limited by the costs and complexity of the necessary instrumentation, data analysis tools, and software. This issue could be overcome by developing more cost-efficient and user-friendly methods that let breeders, farmers, and stakeholders access the benefits of HTPP. To assist such efforts, this thesis presents an ensemble of dedicated affordable phenotyping methods using RGB imaging for a range of key applications under controlled conditions.  The affordable Phenocave imaging system for use in controlled conditions was developed to facilitate studies on the effects of abiotic stresses by gathering data on important plant characteristics related to growth, yield, and adaptation to growing conditions and cultivation systems. Phenocave supports imaging sensors including visible (RGB), spectroscopic (multispectral and hyperspectral), and thermal imaging. Additionally, a pipeline for RGB image analysis was implemented as a plugin for the free and easy-to-use software ImageJ. This plugin has since proven to be an accurate alternative to conventional measurements that produces highly reproducible results. A subsequent study was conducted to evaluate the effects of heat and drought stress on plant growth and grain nutrient composition in wheat, an important staple cereal in Sweden. The effects of stress on plant growth were evaluated using image analysis, while stress-induced changes in the abundance of key plant compounds were evaluated by analyzing the nutrient composition of grains via chromatography. This led to the discovery of genotypes whose harvest quality remains stable under heat and drought stress. The next objective was to evaluate biotic stress; for this case, the effect of the fungal disease Fusarium head blight (FHB) that affects grain development in wheat was investigated. For this purpose, seed phenotyping parameters were used to determine the components and settings of a statistical model, which predicts the occurrence of FHB. The results reveal that grain morphology evaluations, such as length and width, were found to be significantly affected by the disease. Another study was carried out to estimate the disease severity of the common scab (CS) in potatoes, a widely popular food source. CS occurs on the tubers and reduces their visual appeal, significantly affecting their market value. Tubers were analyzed by a deep learning-based method to estimate disease lesion areas caused by CS. Results showed a high correlation between the predictions and expert visual scorings of the disease and proved to be a potential tool for the selection of genotypes that fulfill the market standards and resistance to CS. Both case studies highlight the role of imaging in plant health monitoring and its integration into the larger picture of plant health management.  The methods presented in this work are a starting point for bridging the gap between costs and accessibility to imaging technology. These are affordable and user-friendly resources for generating pivotal knowledge on plant development and genotype selection. In the future, image acquisition of all the methods can be integrated into the Phenocave system, potentially allowing for a more automated and efficient plant health monitoring process, leading to the identification of tolerant genotypes to biotic and abiotic stresses

Development of new tools and germplasms for improvement of wheat resistance to Fusarium head blight

Doctor of PhilosophyDepartment of AgronomyGuihua BaiXiaomao LinWheat Fusarium head blight (FHB) is a devastating disease of wheat worldwide, which can significantly reduce grain yield and quality. Although the application of fungicides can reduce FHB damage, growing FHB resistant wheat is the most effective and eco-friendly approach to reduce the losses. To develop locally adapted FHB-resistant hard winter wheat germplasm, we transferred three major QTLs: Fhb1, Qfhs.ifa-5A, and Qfhb.rwg-5A.2 into two hard winter wheat cultivars, ‘Everest’ and ‘Overland’, using marker-assisted backcrossing and multiplex restriction amplicon sequencing (MRASeq). Ten ‘Overland’ background lines and nine ‘Everest’ background lines with better FHB resistance, recurrent parent similar agronomic traits were selected. They can be used as FHB resistant bridge parents for hard winter wheat breeding. To identify native FHB resistant sources, a population of 201 U.S. breeding lines and cultivars were genotyped using 90K wheat SNP arrays and phenotyped for the percentage of symptomatic spikelets (PSS), Fusarium damaged kernels (FDK) and deoxynivalenol (DON), a toxin produced by the pathogen. Genome-wide association studies (GWAS) identified significant trait associations with single nucleotide polymorphisms (SNPs) on chromosomes 1A, 1D, 2B, 3A, 3B, 4A, 5B and 5D. These marker-trait associations (MTAs) were significant for at least two of the three traits or a single trait in at least two experiments. To accelerate the evaluation of the FDK, we developed an algorithm that can separate FDK from healthy kernels with an accuracy of 90% based on color differences using image processing and unsupervised machine learning methods. Discovery and creation of the new FHB resistant germplasms and development of the fast FDK phenotyping algorithm will accelerate the improvement of U.S. hard winter wheat cultivars for FHB resistance

Detection of Fusarium Head Blight in Wheat Grains Using Hyperspectral and RGB Imaging

In modern agriculture, it is imperative to ensure that crops are healthy and safe for consumption. Fusarium Head Blight (FHB) can cause significant damage to wheat grains by reducing essential components such as moisture, protein, and starch, while also introducing dangerous toxins. Therefore, accurately distinguishing between healthy and FHB-infected wheat grains is essential to guarantee stable and reliable wheat production while limiting financial losses and ensuring food safety. This thesis proposes effective methods to classify healthy and FHB infected wheat grains using Hyperspectral Imaging (HSI) and Red Green Blue (RGB) images. The approach includes a combination of Principal Component Analysis (PCA) with morphology, in addition to dark and white reference correction, to create masks for grains in each image. The classification for the hyperspectral images was achieved using a Partial Least Squares Discriminant Analysis (PLS-DA) model for hyperspectral images and a Convolutional Neural Network (CNN) model for RGB images. Both object-based and pixel-based approaches were compared for the PLS-DA model. The results indicated that the object-based approach outperformed the pixel-based approach and other well-known machine learning algorithms, including Random Forest (RF), linear Support Vector Machine (SVM), Stochastic Gradient Descent (SGD) calibrated one-vs-all and DecisionTree. The PLS-DA model using the object-based method yielded better results when tested on all wheat varieties, achieving an F1-score of 99.4%. Specific wavelengths were investigated based on a loading plot, and four effective wavelengths were identified, 953 nm, 1373 nm, 1923 nm and 2493 nm, with classification accuracy found to be similar to the full spectral range. Moreover, the moisture and water content in the grains were analyzed using hyperspectral images through an aquagram, which demonstrated that healthy grains exhibited higher absorbance values than infected grains for all Water Matrix Coordinates (WAMACS). Furthermore, the CNN model was trained on cropped individual grains, and the classification accuracy was similar to the PLS-DA model, with an F1- score of 98.1%. These findings suggest that HSI is suitable for identifying FHB-infected wheat grains, while RGB images may provide a cost-effective alternative to hyperspectral images for this specific classification task. Further research should consider to explore the potential benefits of HSI for deeper investigations into how water absorption affects spectral measurements and moisture content in grains, in addition to user-friendly interfaces for deep learning based image classification

Controlling Fusarium Head Blight in oat

Oats (Avena sativa) is a versatile crop grown worldwide for animal feed and human consumption. Humanoat consumption has recently risen due to its various health benefits. However, oats are susceptible toFusarium head blight (FHB) caused by various Fusarium fungi. FHB reduces yield and leads to mycotoxinaccumulation. The most commonly reported mycotoxins in oat are trichothecenes deoxynivalenol (DON)and T-2/HT-2 toxins. Trichothecenes inhibit eukaryotic protein biosynthesis and cause acute and chronictoxicoses in human and animals. Effective control of FHB is important for ensuring safety and quality ofoats. This thesis examines various aspects of FHB in oats, relevant to the development of better FHBcontrol strategies.Accurate FHB symptom identification is crucial for breeding resistant oats, but the symptoms of FHB arecryptic, causing errors in scoring the disease during trials. This work presents an affordable method forassessing FHB symptoms in oats by de-hulling mature seeds. Symptoms of blackening and discolorationof the oat kernels significantly correlate with Fusarium DNA and mycotoxin accumulation and thus canbe used as quantitative disease indicators.To enhance pathogen resistance, identifying and characterizing plant resistance genes is key. In thiswork two oat genes coding for DON-detoxifying UDP-glucosyltransferases (UGTs) were identified andcharacterised. Transcripts of two oat UGTs were highly upregulated in response to DON treatment andF.graminearum infection. The genes conferred resistance to several trichothecenes when expressed inyeast. Both UGTs, recombinantly expressed in E.coli were confirmed for their ability to detoxify DON.These genes could potentially be used for developing genetic markers for FHB resistance in oat.Further in this thesis, biocontrol possibilities for FHB in oats are investigated. The fungal BCAClonostachys rosea's potential against FHB is examined. Treating oat spikelets with C. rosea reducedFusarium DNA and DON content in mature kernels. C.rosea enhanced both rate of DON detoxificationand expression of DON-detoxifying UGTs. Furthermore, there was significant upregulation of markers ofinduced resistance, including PR proteins and the WRKY23 transcription factor, indicating that thebiocontrol effect of C. rosea is attributed to the induction of plant defences.Additionally, oats' own endophytes were explored for FHB biocontrol. Fungal endophytes from oatspikelets were isolated and tested for reducing FHB in greenhouse trials. The most successful isolatePseudozyma flocculosa significantly reduced FHB symptoms, F. graminearum biomass, and DONaccumulation in oat. Treatment of oat with P. flocculosa induced expression of genes encoding for PRproteins, known to be involved in FHB resistance

Multi-sensor and data fusion approach for determining yield limiting factors and for in-situ measurement of yellow rust and fusarium head blight in cereals

The world’s population is increasing and along with it, the demand for food. A novel parametric model (Volterra Non-linear Regressive with eXogenous inputs (VNRX)) is introduced for quantifying influences of individual and multiple soil properties on crop yield and normalised difference vegetation Index. The performance was compared to a random forest method over two consecutive years, with the best results of 55.6% and 52%, respectively. The VNRX was then implemented using high sampling resolution soil data collected with an on-line visible and near infrared (vis-NIR) spectroscopy sensor predicting yield variation of 23.21%. A hyperspectral imager coupled with partial least squares regression was successfully applied in the detection of fusarium head blight and yellow rust infection in winter wheat and barley canopies, under laboratory and on-line measurement conditions. Maps of the two diseases were developed for four fields. Spectral indices of the standard deviation between 500 to 650 nm, and the squared difference between 650 and 700 nm, were found to be useful in differentiating between the two diseases, in the two crops, under variable water stress. The optimisation of the hyperspectral imager for field measurement was based on signal-to-noise ratio, and considered; camera angle and distance, integration time, and light source angle and distance from the crop canopy. The study summarises in the proposal of a new method of disease management through suggested selective harvest and fungicide applications, for winter wheat and barley which theoretically reduced fungicide rate by an average of 24% and offers a combined saving of the two methods of £83 per hectare

The Fusarium Mycotoxins in Finnish Cereal Grains : How to Control and Manage the Risk

The central goal of grain cultivation is the production of high-quality food or feed-related raw materials for the processing industry. Management of Fusarium mycotoxins in Finnish cereal grains have a direct impact on human and animal health, and the confidence in a safe and healthy domestic cereals and cereal products. Fusarium fungi and head blight have always emerged in Finland after rainy and poor summer weather conditions. During the 1960s and 1970s the spectrum of Fusarium species and the ability of the fungi to produce mycotoxins in domestic grain were subject to extensive investigation. The summer of 1987 was again very rainy and cold, and there was abundant and even visible occurrence of Fusarium head blight in grains. A decade passed, and another very rainy and cold summer was encountered in 1998. The last straw of the risk of mycotoxins in magnitude was the summers 2012 and 2013. Even up to a quarter and a fifth of domestic oats in grain trading, respectively, were not accepted for food use because of DON concentrations exceeded the EU limit. The aims of the present study were to produce updated information of Fusarium species, and to define the changes in Fusarium mycotoxins in Finnish cereal grains in the years 1987-2014. Another important aims were to determine the basis of the toxin contents and agronomic factors behind the studied samples how to control and manage the Fusarium mycotoxin risk, and to predict by modeling the magnitude of the mycotoxin risk. According to the results, the most common Fusarium species in Finnish cereal grains were F. avenaceum, F. culmorum, F. graminearum, F. poae, F. sporotrichioides and F. langsethiae. When compared to previous studies from the 1970s and 1980s to the present day in Finland, a clear conclusion was drawn that during these years F. graminearum, F. sporotrichioides and F. langsethiae have come strongly into the picture. The number of exceptionally high DON concentrations and also the contents and positive findings of T-2 and HT-2 toxins have increased in Finland. The following important control and management factors were emphasized: pay attention to the quality of seed and seed dressing; rotation - repeated cultivation of cereals is not recommended; careful timing of harvest and harvest drying - moisture content < 14 %; introduction of rapid test methods and sorting technology at farm level, and last but not least, minimize the risks of toxins by cultivation. Industrial sorting and dehulling reduced the DON, T-2+HT-2 and 3-AcDON levels in oat samples by 75–91%, 87 %, and 67–91%, respectively. In the near future, increased collaboration among farmers, researchers, the grain processing industry and consumers is needed. Especially, there is a significant need to increase the competitiveness and cost-effectiveness of grain farming in the specialization of national and international markets, and make producers committed to the production of quality grains, novel utilization of by-products and recycling of nutrients. Among the cereals investigated, oats is the most susceptible to Fusarium infestation and the production of Fusarium mycotoxins in Finland. The market is eagerly looking for new high-yielding varieties capable of preventing Fusarium infestation and having low levels of mycotoxins.Viljan alkutuotannon tärkein tavoite on korkealaatuisen raaka-aineen tuottaminen elintarvike- ja rehuteollisuuden, kotieläintuotannon sekä muiden loppukäyttäjien tarpeisiin. Viljojen hometoksiinien hallinnalla varmistetaan kuluttajien ja eläinten hyvinvointi sekä luottamus turvalliseen ja terveelliseen kotimaiseen viljaan ja viljatuotteisiin. Fusarium -sienet ja punahomeet ovat Suomessa nousseet esiin aina sateisten ja kosteiden sääolosuhteiden jälkeen. 1960- ja 1970 -luvuilla havaittiin, että punahomeet heikentävät jyvien kehitystä, alentavat itävyyttä, aiheuttavat tyvitauteja ja tähkäfusarioosia. Vuosi 1987 oli hyvin sateinen ja kylmä kesä ja punahometta esiintyi viljoissa runsaasti. Kului vuosikymmen, ja koettiin jälleen erittäin sateinen ja kylmä kesä vuonna 1998. Sateinen kesä 2012 oli myös hyvin poikkeuksellinen. Toksiinitasot kohosivat ja kaurasadosta neljännes ei viljan vastaanotossa täyttänyt elintarvikeviljalle säädettyjä suurimpia sallittuja DON-pitoisuuksia. Vuosi 2013 oli lähes yhtä huono hometoksiinien osalta; viidennes kauraeristä ei täyttänyt DONin osalta elintarvikeviljalle säädettyjä enimmäispitoisuuksia. Tämä nosti riskin hallinnan työkalut erityiseen tarkkailuun. Tämän tutkimuksen tavoitteena oli tuottaa ajantasainen tieto suomalaisen viljan Fusarium - sienten lajikirjosta, sekä selvittää viljojen hometoksiinien pitoisuuksien muutokset aikavälillä 1987– 2014. Lisäksi erityisenä tavoitteena oli tutkia miten hometoksiinien muodostumista viljoissa voidaan estää ja hallita eri viljelyteknisillä toimenpiteillä ja mallintamalla. Yleisimmät Fusarium -sienet suomalaisessa viljassa olivat F. avenaceum, F. culmorum, F. graminearum, F. poae, F. sporotrichioides ja F. langsethiae. Kun verrataan näitä tuloksia Fusarium -lajikirjoon 1970-luvulla ja 1980-luvulla, niin selkeästi F. graminearum, F. sporotrichioides ja F. langsethiae ovat nousseet vahvoiksi Fusarium -toksiinien tuottajiksi 2000 -luvulla. Nämä muutokset näkyvät myös korkeiden DON -toksinipitoisuuksien määrän - ja T-2+HT-2 toksiinien pitoisuustasojen kasvussa sekä positiivisten löydösten lisääntymisessä. Tärkeimmät riskinhallinnan työkalut ovat siemenen kunnostus ja peittaus, viljelykierto, puintiajankohdan valinta, ja puidun sadon nopea ja huolellinen kuivatus alle 14 %:iin; pikamittausten ja lajittelun käyttöönotto tilatasolla sekä panostaminen elinvoimaisen ja satoisan kasvuston aikaansaamiseksi. Lajittelulla ja kuorinnalla kauranäytteiden DON-, T-2+HT-2- ja 3- AcDON-pitoisuudet alenivat 75–91 %, 87 % ja 67–91 % vastaavassa järjestyksessä. Merkittävä tarve on lisätä viljan viljelijöiden kilpailukykyä ja kustannustehokkuutta erikoistuvilla kansallisilla ja kansainvälisillä markkinoilla sekä sitouttaa viljelijät laatuviljan tuotantoon, sivuvirtojen uuteen hyödyntämiseen ja ravinteiden kierrätykseen. Kaikista tutkituista viljanäytteistä kaura oli herkin Fusarium -sienten tartunnalle ja Fusarium -toksiinien muodostumiselle. Tulevaisuuden viljamarkkinat odottavat herkeämättä uusia satoisia Fusarium - kestäviä lajikkeita.Siirretty Doriast