SoybeanNet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (UAV) Images

Soybeans are a critical source of food, protein and oil, and thus have received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques. Within this context, soybean pod counting plays an essential role in understanding and optimizing production. Despite recent advancements, the development of a robust pod-counting algorithm capable of performing effectively in real-field conditions remains a significant challenge This paper presents a pioneering work of accurate soybean pod counting utilizing unmanned aerial vehicle (UAV) images captured from actual soybean fields in Michigan, USA. Specifically, this paper presents SoybeanNet, a novel point-based counting network that harnesses powerful transformer backbones for simultaneous soybean pod counting and localization with high accuracy. In addition, a new dataset of UAV-acquired images for soybean pod counting was created and open-sourced, consisting of 113 drone images with more than 260k manually annotated soybean pods captured under natural lighting conditions. Through comprehensive evaluations, SoybeanNet demonstrated superior performance over five state-of-the-art approaches when tested on the collected images. Remarkably, SoybeanNet achieved a counting accuracy of $84.51\%$ when tested on the testing dataset, attesting to its efficacy in real-world scenarios. The publication also provides both the source code (\url{https://github.com/JiajiaLi04/Soybean-Pod-Counting-from-UAV-Images}) and the labeled soybean dataset (\url{https://www.kaggle.com/datasets/jiajiali/uav-based-soybean-pod-images}), offering a valuable resource for future research endeavors in soybean pod counting and related fields.Comment: 12 pages, 5 figure

Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean [Glycine max L. (Merr.)] pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot. To meet this goal, we developed a multi-view image-based yield estimation framework utilizing deep learning architectures. Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions. We used data from controlled imaging environment in field, as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation. Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort, and opening new breeding methods avenues to develop cultivars

Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in Soybean

The rate of advancement made in phenomic-assisted breeding methodologies has lagged those of genomic-assisted techniques, which is now a critical component of mainstream cultivar development pipelines. However, advancements made in phenotyping technologies have empowered plant scientists with affordable high-dimensional datasets to optimize the operational efficiencies of breeding programs. Phenomic and seed yield data was collected across six environments for a panel of 292 soybean accessions with varying genetic improvements. Random forest, a machine learning (ML) algorithm, was used to map complex relationships between phenomic traits and seed yield and prediction performance assessed using two cross-validation (CV) scenarios consistent with breeding challenges. To develop a prescriptive sensor package for future high-throughput phenotyping deployment to meet breeding objectives, feature importance in tandem with a genetic algorithm (GA) technique allowed selection of a subset of phenotypic traits, specifically optimal wavebands. The results illuminated the capability of fusing ML and optimization techniques to identify a suite of in-season phenomic traits that will allow breeding programs to decrease the dependence on resource-intensive end-season phenotyping (e.g., seed yield harvest). While we illustrate with soybean, this study establishes a template for deploying multitrait phenomic prediction that is easily amendable to any crop species and any breeding objective

Improved weed management for transplanted aman rice

Rainfed transplanted rice grown in the monsoon aman season accounts for more than 50% of the total area planted to rice in Bangladesh. Because of rising input costs, including labor, farmers are searching for ways to maintain income, by either increasing yields or reducing costs or both. On-farm trials in the High Barind Tract indicated that one-third of the farmers would be able to gain 0.5 t ha–1 or more additional grain by undertaking more intensive or timelier weeding than is usual under current management practices. Higher yields were observed on-farm from a preemergence application of butachlor(1.25 kg a.i. ha–1) compared with hand weeding twice. In Comilla District, trials of a range of weed management practices demonstrated that the yield advantage over the farmers’ practice, either one or two hand weedings, was on average 355 ± 18 kg ha–1 for Rifit (pretilachlor), 281 ± 39 kg ha–1 for Machete (butachlor), and 210 ± 34 kg ha–1 for Ronstar (oxadiazon), each followed by one hand weeding in aman 2003. Partial budgets calculated for inputs and returns showed that hand weeding was less profitable than herbicides in rainfed rice,incurring US$72 ha–1 lower return. Use of a push weeder plus one hand weeding was less profitable than herbicides, incurring$49 ha–1 lower return. To date, herbicides have been largely promoted for irrigated rice in Bangladesh. The trial results demonstrate that under rainfed conditions early in the aman season, water levels are adequate for herbicides to work effectively. The use of herbicides allows timely weed control when there is a shortage of labor and avoids transaction costs, such as the provision of meals and time needed to source laborers. Herbicides are likely to be adopted by growers experiencing labor shortages, particularly on large farms and for farmers seeking to reduce input costs. Sharecroppers and tenant farmers who pay rent are primarily concerned about obtaining a high aman yield, so innovations that raise aman yields (such as herbicides that will have a similar effect as a timely first weeding) are also likely to be adopted on sharecropped plots, even when costs are not shared between the landlord and tenant

Agro-morphological characterization of pigeonpea (Cajanus cajan L. Millspaugh) landraces grown in Benin: Implications for breeding and conservation

Pigeonpea (Cajanus cajan L. Millspaugh) is a neglected and under-utilized crop consumed in several regions of word. In order to assess performance of pigeonpea landraces grown in Benin for useful breeding programs, 50 accessions were collected from 39 villages. These accessions were characterized by using 12 qualitative and 11 quantitative traits. Based on the seeds morphological characteristics, the 50 accessions were grouped in 12 morphotypes. However, 8 morphological classes were obtained with cluster analysis based on the unweighted pair group method with arithmetic average method using qualitative traits, whereas in principal component analysis only 5 clusters have been obtained using quantitative traits. The association/correlation among quantitative characters showed that grain yield was negatively correlated with pod width, days to 50% flowering and physiological maturity while it was positively correlated with pod length, pods per plant, branches per plant and number of seeds per pod. Based on four quantitative traits (number of pods per plant, number of seeds per pod, 100 seed weight, and early maturity), the 23 accessions from cluster 3 of whom kk5 (Ekloui), kk8 (Nontchiovi kloui), kk15 (Otili founfoun), kk18 (Klouékoun wéwé), kk22 (Otili), kk23 (CA monlikoun) and kk28 (Hounkoun wéwé) have been recommended as good sources of germplasm for improving the pigeonpea productivity. Further characterization using molecular techniques as well as conservation attention should be conducted to confirm the present result and maintain the germplasm for future breeding programs

Integrating genotype and weather variables for soybean yield prediction using deep learning

Realized performance of complex traits is dependent on both genetic and environmental factors, which can be difficult to dissect due to the requirement for multiple replications of many genotypes in diverse environmental conditions. To mediate these problems, we present a machine learning framework in soybean (Glycine max (L.) Merr.) to analyze historical performance records from Uniform Soybean Tests (UST) in North America, with an aim to dissect and predict genotype response in multiple envrionments leveraging pedigree and genomic relatedness measures along with weekly weather parameters. The ML framework of Long Short Term Memory - Recurrent Neural Networks works by isolating key weather events and genetic interactions which affect yield, seed oil, seed protein and maturity enabling prediction of genotypic responses in unseen environments. This approach presents an exciting avenue for genotype x environment studies and enables prediction based systems. Our approaches can be applied in plant breeding programs with multi-environment and multi-genotype data, to identify superior genotypes through selection for commercial release as well as for determining ideal locations for efficient performance testing

Prediction of Soybean Plant Density Using a Machine Learning Model and Vegetation Indices Extracted from RGB Images Taken with a UAV

Soybean plant density is an important factor of successful agricultural production. Due to the high number of plants per unit area, early plant overlapping and eventual plant loss, the estimation of soybean plant density in the later stages of development should enable the determination of the final plant number and reflect the state of the harvest. In order to assess soybean plant density in a digital, nondestructive, and less intense way, analysis was performed on RGB images (containing three channels: RED, GREEN, and BLUE) taken with a UAV (Unmanned Aerial Vehicle) on 66 experimental plots in 2018, and 200 experimental plots in 2019. Mean values of the R, G, and B channels were extracted for each plot, then vegetation indices (VIs) were calculated and used as predictors for the machine learning model (MLM). The model was calibrated in 2018 and validated in 2019. For validation purposes, the predicted values for the 200 experimental plots were compared with the real number of plants per unit area (m(2)). Model validation resulted in the correlation coefficient-R = 0.87, mean absolute error (MAE) = 6.24, and root mean square error (RMSE) = 7.47. The results of the research indicate the possibility of using the MLM, based on simple values of VIs, for the prediction of plant density in agriculture without using human labor

Genetic variability, path coefficient and marker-trait association analysis for resistance to rosette disease in groundnut.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Several abiotic, biotic and socio-economic aspects constrain the production of groundnut (Arachis hypogea L.). Groundnut rosette disease (GRD) which can cause yield losses of up to 100% in susceptible cultivars, is among the most important biotic stresses. The use of resistant cultivars is the most viable method to control the disease, therefore, breeding for high yielding and GRD resistant cultivars is needed and should be a priority. The present study was conducted to: (i) determine genetic variability for GRD response and yield traits in selected groundnut accessions under natural infestation, (ii) assess the relationship between seed yield and its related traits, and analyse agro-morphological diversity in selected groundnut accessions under natural GRD infestation and (iii) evaluate groundnut recombinant inbred lines for resistance to GRD and perform SNP marker-trait association analysis. Twentyfive groundnut accessions and three controls were evaluated under natural GRD infestation to assess genetic variability for GRD response and yield related traits. Seed yield, number of pods per plant, plant height, GRD incidence and number of secondary branches showed high phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV), while moderate variation (PCV and GCV) was observed for days to flowering and pod width. A combination of high heritability and genetic advance was recorded for number of secondary branches, plant height, seed yield and GRD incidence, indicating that phenotypic selection based on the mean would be successful in improving these traits. Phenotypic correlations and sequential path analysis indicated that high seed yield was directly associated with taller genotypes, higher number of pods per plant and hundred seed weight, which were a result of higher pod width and lower GRD incidence. Based on morphological traits, the evaluated accessions were grouped into four clusters. Days to flowering and maturity, number of branches, plant height, number of pods per plant, pod width and length, seed yield and GRD incidence, largely influenced this variation. Principal component analysis (PCA) biplot was effective in showing the genetic distance among the accessions with results consistent to those of the cluster analysis. Moreover, Shannon-Weaver diversity indices (0.949-0.9996) for qualitative traits also indicated the existence of high diversity among the accessions. A total of 25 groundnut genotypes, which comprised 21 RILs derived from a bi-parental cross, both parents, and two susceptible controls (CG7 and JL24) were evaluated and used to perform SNP marker-trait association analysis for resistance to GRD. There were significant differences among the lines in all recorded traits, indicating the existence of genetic variability and possibility of effective selection. Interaction of genotype and environment was significant for disease incidence and the glasshouse environment had higher disease pressure, providing the best discrimination among the tested genotypes. ICGV-SM 15605, ICGV-SM 15621, ICGV-SM 15618, ICGV-SM 15604 and ICGV-SM 15615 were among the resistant and high yielding RILs. Twenty-two highly significant marker-trait associations were identified, which will add to previously reported genomic regions influencing GRD and the aphid vector resistance. Overall, the study showed that taller genotypes, higher number of pods per plant and hundred seed weight can be used to improve seed yield in groundnut, particularly under GRD infestation. The genetic diversity among the accessions provides an opportunity for parent selection that can be used for breeding high yielding and GRD resistant cultivars. In addition, the SNP markers will be useful in classifying groundnut germplasm based on the GRD response and for their use in marker-assisted selection, once validated

Arkansas Soybean Research Studies 2015

Arkansas is the leading soybean-producing state in the mid-southern United States. Arkansas ranked 10th in soybean production in 2015 when compared to the other soybean-producing states in the U.S. The state represents 4.0% of the total U.S. soybean production and 3.7% of the total acres planted to soybean in 2015. The 2015 state soybean average was 49 bushels per acres, 0.5 bushel per acres less than the state record soybean yield set in 2014 (Table 1). The top five soybean-producing counties in 2015 were Mississippi, Desha, Poinsett, Phillips, and Arkansas Counties. These five counties accounted for 35% of soybean production in Arkansas in 2015

Environmental advantages and potential of faba bean (Vicia faba L.) cultivation in Norway, with a special focus on C and N inventory

Faba bean (Vicia faba L.) is a legume capable of BNF that has the potential to play a role in achieving climate goals while influencing the future of Norwegian agriculture. The GreenPlantFood project is exploring ways to improve sustainability of the farm to fork food system in Norway, and this thesis seeks to contribute both agronomic and plant residue information on faba bean grown in Norway. Faba bean is a high protein crop, and comparisons of food production in Norway have shown that faba bean production has 14-29 times lower GHG emissions per kg protein compared to dairy beef meat, and 9-15 times lower GHG emissions compared to milk. Faba bean also provides agronomic and environmental benefits in cultivation. Up to 200 kg N ha-1 N fertilizer can be saved in crops grown after faba bean, and grain yields respond positively to faba bean residue compared to other grain residue. BNF capabilities are thought to play a role in this, but the specific mechanisms behind these benefits are not completely understood. A collection of promising faba bean varieties were sown on two different dates (27 April, 14 May) at Vollebekk research farm in Ås. Two plots were sowed for each variety in both sowing times. A selection of 10 varieties from the 14 May sowing time were selected for full plant analysis. 0.5 m2 subplots were used for each of the 10 selected varieties. Plants from each subplot were harvested, measured, and divided into seeds, stems, pods, and leaves for nitrogen and carbon analysis. Yield data from the full plots showed very little statistically significant differences between varieties, but sowing time had a significant effect on dry seed weight (p-value < 0.001). Mean dry seed weight for plants sown 27 April (6.63 kg) was significantly higher than mean dry seed weight for plants sown 14 May (5.18 kg). The plants sown 27 April received 42.9 °C extra degree days, and 35.5 mm additional precipitation. Subplot plant analysis showed that seeds had 4.59% N, leaves had 2.40% N, pods had 1.37% N, and stems had 0.69% N. These N contents support previous findings regarding faba bean residue nitrogen content. Carbon contents of plant residue parts were all roughly 40%. Of the plant material included in post-harvest plant residue, only leaves had above the 2% nitrogen content threshold that is necessary for successful nitrogen mineralization. Harvest index and residue nitrogen content were negatively correlated (Pearson’s r = - 0.776). High harvest index is important for the plant protein function of faba bean, and high residue nitrogen content is a valuable factor faba bean’s role in crop rotations.M-P