How useful is Active Learning for Image-based Plant Phenotyping?

Deep learning models have been successfully deployed for a diverse array of image-based plant phenotyping applications including disease detection and classification. However, successful deployment of supervised deep learning models requires large amount of labeled data, which is a significant challenge in plant science (and most biological) domains due to the inherent complexity. Specifically, data annotation is costly, laborious, time consuming and needs domain expertise for phenotyping tasks, especially for diseases. To overcome this challenge, active learning algorithms have been proposed that reduce the amount of labeling needed by deep learning models to achieve good predictive performance. Active learning methods adaptively select samples to annotate using an acquisition function to achieve maximum (classification) performance under a fixed labeling budget. We report the performance of four different active learning methods, (1) Deep Bayesian Active Learning (DBAL), (2) Entropy, (3) Least Confidence, and (4) Coreset, with conventional random sampling-based annotation for two different image-based classification datasets. The first image dataset consists of soybean [Glycine max L. (Merr.)] leaves belonging to eight different soybean stresses and a healthy class, and the second consists of nine different weed species from the field. For a fixed labeling budget, we observed that the classification performance of deep learning models with active learning-based acquisition strategies is better than random sampling-based acquisition for both datasets. The integration of active learning strategies for data annotation can help mitigate labelling challenges in the plant sciences applications particularly where deep domain knowledge is required

Leveraging Large Language Models and Weak Supervision for Social Media data annotation: an evaluation using COVID-19 self-reported vaccination tweets

The COVID-19 pandemic has presented significant challenges to the healthcare industry and society as a whole. With the rapid development of COVID-19 vaccines, social media platforms have become a popular medium for discussions on vaccine-related topics. Identifying vaccine-related tweets and analyzing them can provide valuable insights for public health research-ers and policymakers. However, manual annotation of a large number of tweets is time-consuming and expensive. In this study, we evaluate the usage of Large Language Models, in this case GPT-4 (March 23 version), and weak supervision, to identify COVID-19 vaccine-related tweets, with the purpose of comparing performance against human annotators. We leveraged a manu-ally curated gold-standard dataset and used GPT-4 to provide labels without any additional fine-tuning or instructing, in a single-shot mode (no additional prompting)

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

Advances in Cereal Crops Breeding

This Special Issue on ‘Advances in Cereal Crops Breeding’ comprises 10 papers covering a wide range of subjects, including the expression-level investigation of genes in terms of salinity stress adaptations and their relationships with proteomics in rice, the use of genetic analysis to assess the general combining ability (GCA) and specific combining ability (SCA) in promising hybrids of maize, the use of DNA markers based on PCR in rice, the identification of quantitative trait loci (QTLs) in wheat and simple sequence repeats (SSR) in rice, the use of single-nucleotide polymorphisms (SNP) in a genome-wide association study (GWAS) in cereals, and Nanopore direct RNA sequencing of related with LTR RNA retrotransposon in triticale prior to the genomic selection of heterotic maize hybrids

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

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