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

Topological Interface-State Lasing in a Polymer-Cholesteric Liquid Crystal Superlattice

The advance of topological photonics has heralded a revolution for manipulating light as well as for the development of novel photonic devices such as topological insulator lasers. Here, we demonstrate topological lasing of circular polarization in a polymer-cholesteric liquid crystal (P-CLC) superlattice, tunable in the visible wavelength regime. By use of the femtosecond-laser direct-writing and self-assembling techniques, we establish the P-CLC superlattice with a controlled mini-band structure and a topological interface defect, thereby achieving a low threshold for robust topological lasing at about 0.4 uJ. Thanks to the chiral liquid crystal, not only the emission wavelength is thermally tuned, but the circularly polarized lasing is readily achieved. Our results bring about the possibility to realize compact and integrated topological photonic devices at low cost, as well as to engineer an ideal platform for exploring topological physics that involves light-matter interaction in soft-matter environments.Comment: 14 pages, 4 figure

Transcriptomic analysis reveals ethylene signal transduction genes involved in pistil development of pumpkin

Development of female flowers is an important process that directly affects the yield of Cucubits. Little information is available on the sex determination and development of female flowers in pumpkin, a typical monoecious plant. In the present study, we used aborted and normal pistils of pumpkin for RNA-Seq analysis and determined the differentially expressed genes (DEGs) to gain insights into the molecular mechanism underlying pistil development in pumpkin. A total of 3,817 DEGs were identified, among which 1,341 were upregulated and 2,476 were downregulated. The results of transcriptome analysis were confirmed by real-time quantitative RT-PCR. KEGG enrichment analysis showed that the DEGs were significantly enriched in plant hormone signal transduction and phenylpropanoid biosynthesis pathway. Eighty-four DEGs were enriched in the plant hormone signal transduction pathway, which accounted for 12.54% of the significant DEGs, and most of them were annotated as predicted ethylene responsive or insensitive transcription factor genes. Furthermore, the expression levels of four ethylene signal transduction genes in different flower structures (female calyx, pistil, male calyx, stamen, leaf, and ovary) were investigated. The ethyleneresponsive DNA binding factor, ERDBF3, and ethylene responsive transcription factor, ERTF10, showed the highest expression in pistils and the lowest expression in stamens, and their expression levels were 78- and 162-times more than that in stamens, respectively. These results suggest that plant hormone signal transduction genes, especially ethylene signal transduction genes, play an important role in the development of pistils in pumpkin. Our study provides a theoretical basis for further understanding of the mechanism of regulation of ethylene signal transduction genes in pistil development and sex determination in pumpkin

High-Throughput Construction of Intron-Containing Hairpin RNA Vectors for RNAi in Plants

With the wide use of double-stranded RNA interference (RNAi) for the analysis of gene function in plants, a high-throughput system for making hairpin RNA (hpRNA) constructs is in great demand. Here, we describe a novel restriction-ligation approach that provides a simple but efficient construction of intron-containing hpRNA (ihpRNA) vectors. The system takes advantage of the type IIs restriction enzyme BsaI and our new plant RNAi vector pRNAi-GG based on the Golden Gate (GG) cloning. This method requires only a single PCR product of the gene of interest flanked with BsaI recognition sequence, which can then be cloned into pRNAi-GG at both sense and antisense orientations simultaneously to form ihpRNA construct. The process, completed in one tube with one restriction-ligation step, produced a recombinant ihpRNA with high efficiency and zero background. We demonstrate the utility of the ihpRNA constructs generated with pRNAi-GG vector for the effective silencing of various individual endogenous and exogenous marker genes as well as two genes simultaneously. This method provides a novel and high-throughput platform for large-scale analysis of plant functional genomics

Elevation of the Yields of Very Long Chain Polyunsaturated Fatty Acids via Minimal Codon Optimization of Two Key Biosynthetic Enzymes

Eicosapentaenoic acid (EPA, 20:5Δ5,8,11,14,17) and Docosahexaenoic acid (DHA, 22:6Δ4,7,10,13,16,19) are nutritionally beneficial to human health. Transgenic production of EPA and DHA in oilseed crops by transferring genes originating from lower eukaryotes, such as microalgae and fungi, has been attempted in recent years. However, the low yield of EPA and DHA produced in these transgenic crops is a major hurdle for the commercialization of these transgenics. Many factors can negatively affect transgene expression, leading to a low level of converted fatty acid products. Among these the codon bias between the transgene donor and the host crop is one of the major contributing factors. Therefore, we carried out codon optimization of a fatty acid delta-6 desaturase gene PinD6 from the fungus Phytophthora infestans, and a delta-9 elongase gene, IgASE1 from the microalga Isochrysis galbana for expression in Saccharomyces cerevisiae and Arabidopsis respectively. These are the two key genes encoding enzymes for driving the first catalytic steps in the Δ6 desaturation/ Δ6 elongation and the Δ9 elongation/Δ8 desaturation pathways for EPA/DHA biosynthesis. Hence expression levels of these two genes are important in determining the final yield of EPA/DHA. Via PCR-based mutagenesis we optimized the least preferred codons within the first 16 codons at their N-termini, as well as the most biased CGC codons (coding for arginine) within the entire sequences of both genes. An expression study showed that transgenic Arabidopsis plants harbouring the codon-optimized IgASE1 contained 64% more elongated fatty acid products than plants expressing the native IgASE1 sequence, whilst Saccharomyces cerevisiae expressing the codon optimized PinD6 yielded 20 times more desaturated products than yeast expressing wild-type (WT) PinD6. Thus the codon optimization strategy we developed here offers a simple, effective and low-cost alternative to whole gene synthesis for high expression of foreign genes in yeast and Arabidopsis

Effects of Micro-rates of 2,4-D and Dicamba on Lettuce and Pumpkin in Nebraska

Off-target herbicide injury from dicamba and 2,4-D is an increasingly common problem for specialty crop growers in the Midwestern United States. Both lettuce (Lactuca sativa L.) and pumpkin (Cucurbita spp.) are common specialty crops grown in Nebraska, and their proximity to corn and soybean production makes these crops susceptible to herbicide drift injury and yield loss. The objectives of this thesis research was to quantify crop injury and yield loss in greenhouse- and field-grown lettuce and field-grown pumpkins at different growth stages after exposure to sub-lethal doses of dicamba or 2,4-D. Dose response curves were generated to determine effective dose (ED) values and to relate drift rates with crop injury and yield loss. In addition, a dicamba residue test was conducted in lettuce to relate residue levels, drift rates, crop injury, and yield loss. Our study found out all modern lettuce varieties ‘Green Forest’, ‘Vulcan’, and ‘Allstar’ were highly susceptible to dicamba and 2,4-D. Mature stage lettuce had higher tolerance for both herbicide but with observed high variation on yield. Some increase in yield was observed in mature stage lettuce but the benefits of the small increase in biomass was offset by visual injury and reduced marketability. 2,4-D choline caused yield reduction on seedling stage ‘Green Forest’ and ‘Vulcan’ at the rate above 21.3 g ae ha-1 with 50% yield loss at the rate of 33.6 g ae ha-1. ‘Green Forest’ at seedling stage were highly susceptible to dicamba with 50% yield loss when treated at the rate of 16.8 g ae ha-1. Pumpkins studies showed less susceptibility to dicamba and 2,4-D at flowering stage with high variability on yield that caused poor lack of fit on the dose-response model. Dicamba at the rate of 139.8 g ae ha-1 and 2,4-D at the rate of 266.6 g ae ha-1 caused significant yield reduction on vegetative pumpkins compared with the control. The results provided information to Nebraska growers and aid to quantify economic loss from off-target herbicide drift events and highlight the need for communication between commercial herbicide applicators and specialty crop growers

Herbicide injury from dicamba and 2,4-D: How much is too much in lettuce?

Off-target herbicide injury from dicamba and 2,4-D is an increasingly common problem for specialty crop growers in the Midwest U.S. Lettuce (Lactuca sativa L.) is a common specialty crops grown in Nebraska, but proximity to corn and soybean production leaves growers vulnerable to crop injury and significant economic loss. The goal of this study was to quantify crop injury and yield loss in greenhouse grown lettuce after exposure to simulated sub-lethal drift rates of 2,4-D and dicamba. Sublethal doses were determined based on a percentage of the maximum labeled rate and ranged from 25% to 0.01%. Tested lettuce cultivars included ‘Green Forest,’ ‘Vulcan,’ and ‘Allstar,’ and each was sprayed at seedling and mature growth stages. Plant injury ratings were recorded every 4 days after herbicide application until harvest, when final fresh weight yield was determined. Dose response curves were generated to determine effective dose (ED) values and to relate drift rates with crop injury and yield loss. All lettuce cultivars were more tolerant of herbicides at the maturity growth stage than at the seedling growth stage. Among cultivars, ‘Green Forest’ was most susceptible to injury from dicamba and 2,4-D. At the seedling stage, 1.4% dicamba caused 50% visual injury and 35% yield loss in ‘Green Forest,’ and 1% 2,4-D caused 10% visual injury and 20% yield loss. When ‘Vulcan’ was sprayed at the seedling stage, 10% 2,4-D led to plant mortality within 16 days of treatment, and 25% dicamba led to plant mortality within 22 days of treatment. Results confirm the susceptibility of lettuce to relatively low rates of 2,4-D and dicamba, which highlights the importance of drift mitigation efforts in the Midwest U.S

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

Bioinspired Design and Computational Prediction of Iron Complexes with Pendant Amines for the Production of Methanol from CO₂ and H₂

Inspired by the active site structure of [FeFe]-hydrogenase, we built a series of iron dicarbonyl diphosphine complexes with pendant amines and predicted their potentials to catalyze the hydrogenation of CO₂ to methanol using density functional theory. Among the proposed iron complexes, [(P^tBu₂N^tBu₂H)­FeH­(CO)₂(COOH)]⁺ (<b>5</b>_<b>COOH</b>) is the most active one with a total free energy barrier of 23.7 kcal/mol. Such a low barrier indicates that <b>5</b>_<b>COOH</b> is a very promising low-cost catalyst for high-efficiency conversion of CO₂ and H₂ to methanol under mild conditions. For comparison, we also examined Bullock’s Cp iron diphosphine complex with pendant amines, [(P^tBu₂N^tBu₂H)­FeHCp^C5F4N]⁺ (<b>5</b>_{<b>Cp‑C5F4N</b>}), as a catalyst for hydrogenation of CO₂ to methanol and obtained a total free energy barrier of 27.6 kcal/mol, which indicates that <b>5</b>_{<b>Cp‑C5F4N</b>} could also catalyze the conversion of CO₂ and H₂ to methanol but has a much lower efficiency than our newly designed iron complexes