Machine Vision-Based Crop-Load Estimation Using YOLOv8

Labor shortages in fruit crop production have prompted the development of mechanized and automated machines as alternatives to labor-intensive orchard operations such as harvesting, pruning, and thinning. Agricultural robots capable of identifying tree canopy parts and estimating geometric and topological parameters, such as branch diameter, length, and angles, can optimize crop yields through automated pruning and thinning platforms. In this study, we proposed a machine vision system to estimate canopy parameters in apple orchards and determine an optimal number of fruit for individual branches, providing a foundation for robotic pruning, flower thinning, and fruitlet thinning to achieve desired yield and quality.Using color and depth information from an RGB-D sensor (Microsoft Azure Kinect DK), a YOLOv8-based instance segmentation technique was developed to identify trunks and branches of apple trees during the dormant season. Principal Component Analysis was applied to estimate branch diameter (used to calculate limb cross-sectional area, or LCSA) and orientation. The estimated branch diameter was utilized to calculate LCSA, which served as an input for crop-load estimation, with larger LCSA values indicating a higher potential fruit-bearing capacity.RMSE for branch diameter estimation was 2.08 mm, and for crop-load estimation, 3.95. Based on commercial apple orchard management practices, the target crop-load (number of fruit) for each segmented branch was estimated with a mean absolute error (MAE) of 2.99 (ground truth crop-load was 6 apples per LCSA). This study demonstrated a promising workflow with high performance in identifying trunks and branches of apple trees in dynamic commercial orchard environments and integrating farm management practices into automated decision-making

Machine Vision System for Early-stage Apple Flowers and Flower Clusters Detection for Precision Thinning and Pollination

Early-stage identification of fruit flowers that are in both opened and unopened condition in an orchard environment is significant information to perform crop load management operations such as flower thinning and pollination using automated and robotic platforms. These operations are important in tree-fruit agriculture to enhance fruit quality, manage crop load, and enhance the overall profit. The recent development in agricultural automation suggests that this can be done using robotics which includes machine vision technology. In this article, we proposed a vision system that detects early-stage flowers in an unstructured orchard environment using YOLOv5 object detection algorithm. For the robotics implementation, the position of a cluster of the flower blossom is important to navigate the robot and the end effector. The centroid of individual flowers (both open and unopen) was identified and associated with flower clusters via K-means clustering. The accuracy of the opened and unopened flower detection is achieved up to mAP of 81.9% in commercial orchard images

Robotic Pollination of Apples in Commercial Orchards

This research presents a novel, robotic pollination system designed for targeted pollination of apple flowers in modern fruiting wall orchards. Developed in response to the challenges of global colony collapse disorder, climate change, and the need for sustainable alternatives to traditional pollinators, the system utilizes a commercial manipulator, a vision system, and a spray nozzle for pollen application. Initial tests in April 2022 pollinated 56% of the target flower clusters with at least one fruit with a cycle time of 6.5 s. Significant improvements were made in 2023, with the system accurately detecting 91% of available flowers and pollinating 84% of target flowers with a reduced cycle time of 4.8 s. This system showed potential for precision artificial pollination that can also minimize the need for labor-intensive field operations such as flower and fruitlet thinning.Comment: 2 Page, 1 figur

Impact of adoption of heat-stress tolerant maize hybrid on yield and profitability: Evidence from Terai region of Nepal

Abiotic stresses (drought, heat) are one of the major impediments to enhancing the maize productivity of marginal farmers in the facet of climate change. The present study attempts to investigate the impact of heat-tolerant maize hybrid on yield and income in the Terai region of Nepal. This study uses cross-sectional farm household-level data collected in August 2021 from a randomly selected sample of 404 rural households. We used a doubly robust inverse probability weighted regression adjustment method to obtain reliable impact estimates. Adoption of heat-tolerant hybrid increases yields by 16% and income by 44% in the spring season (a stress condition). Overall, yield increases by 12%, net income by 31%, saving of 40% in seed costs, and per capita food expenditure increases by 8.50%. Hence a conducive environment must be created for scaling up heat-tolerant maize varieties to increase productivity, minimize risk, and transform of the maize sector

Variation in grain zinc and iron concentrations, grain yield and associated traits of biofortified bread wheat genotypes in Nepal

Wheat (Triticum aestivum L.) is one of the major staples in Nepal providing the bulk of food calories and at least 30% of Fe and Zn intake and 20% of dietary energy and protein consumption; thus, it is essential to improve its nutritional quality. To select high-yielding genotypes with elevated grain zinc and iron concentration, the sixth, seventh, eighth, and ninth HarvestPlus Yield Trials (HPYTs) were conducted across diverse locations in Nepal for four consecutive years: 2015–16, 2016–17, 2017–18, and 2018–19, using 47 biofortified and 3 non-biofortified CIMMYT-bred, bread wheat genotypes: Baj#1, Kachu#1, and WK1204 (local check). Genotypic and spatial variations were found in agro-morphological traits; grain yield and its components; and the grain zinc and iron concentration of tested genotypes. Grain zinc concentration was highest in Khumaltar and lowest in Kabre. Likewise, grain iron concentration was highest in Doti and lowest in Surkhet. Most of the biofortified genotypes were superior for grain yield and for grain zinc and iron concentration to the non-biofortified checks. Combined analyses across environments showed moderate to high heritability for both Zn (0.48–0.81) and Fe (0.46–0.79) except a low heritability for Fe observed for 7th HPYT (0.15). Grain yield was positively correlated with the number of tillers per m2, while negatively correlated with days to heading and maturity, grain iron, grain weight per spike, and thousand grain weight. The grain zinc and iron concentration were positively correlated, suggesting that the simultaneous improvement of both micronutrients is possible through wheat breeding. Extensive testing of CIMMYT derived high Zn wheat lines in Nepal led to the release of five biofortified wheat varieties in 2020 with superior yield, better disease resistance, and 30–40% increased grain Zn and adaptable to a range of wheat growing regions in the country – from the hotter lowland, or Terai, regions to the dry mid- and high-elevation areas

Correction: Structural variation underlies functional diversity at methyl salicylate loci in tomato.

[This corrects the article DOI: 10.1371/journal.pgen.1010751.]

Structural variation underlies functional diversity at methyl salicylate loci in tomato.

Methyl salicylate is an important inter- and intra-plant signaling molecule, but is deemed undesirable by humans when it accumulates to high levels in ripe fruits. Balancing the tradeoff between consumer satisfaction and overall plant health is challenging as the mechanisms regulating volatile levels have not yet been fully elucidated. In this study, we investigated the accumulation of methyl salicylate in ripe fruits of tomatoes that belong to the red-fruited clade. We determine the genetic diversity and the interaction of four known loci controlling methyl salicylate levels in ripe fruits. In addition to Non-Smoky Glucosyl Transferase 1 (NSGT1), we uncovered extensive genome structural variation (SV) at the Methylesterase (MES) locus. This locus contains four tandemly duplicated Methylesterase genes and genome sequence investigations at the locus identified nine distinct haplotypes. Based on gene expression and results from biparental crosses, functional and non-functional haplotypes for MES were identified. The combination of the non-functional MES haplotype 2 and the non-functional NSGT1 haplotype IV or V in a GWAS panel showed high methyl salicylate levels in ripe fruits, particularly in accessions from Ecuador, demonstrating a strong interaction between these two loci and suggesting an ecological advantage. The genetic variation at the other two known loci, Salicylic Acid Methyl Transferase 1 (SAMT1) and tomato UDP Glycosyl Transferase 5 (SlUGT5), did not explain volatile variation in the red-fruited tomato germplasm, suggesting a minor role in methyl salicylate production in red-fruited tomato. Lastly, we found that most heirloom and modern tomato accessions carried a functional MES and a non-functional NSGT1 haplotype, ensuring acceptable levels of methyl salicylate in fruits. Yet, future selection of the functional NSGT1 allele could potentially improve flavor in the modern germplasm

Prevalence and pattern of dyslipidemia in Nepalese individuals with type 2 diabetes

Abstract Background Atherogenic dyslipidemia is an important modifiable risk factor for cardiovascular disease among patients of type 2 diabetes mellitus. Timely detection and characterization of this condition help clinicians estimate future risk of cardiovascular disease and take appropriate preventive measures. The aim of this study was to determine the prevalence, pattern and predictors of dyslipidemia in a cohort of Nepalese patients with type 2 diabetes. Results We found mixed dyslipidemia as the most prevalent (88.1%) and isolated dyslipidemia (10.1%) as the least prevalent forms of dyslipidemia in our patients. The most prevalent form of single dyslipidemia was high LDL-C (73.8%) and combined dyslipidemia was high TG, high LDL-C and low HDL-C (44.7%). Prevalence of all single and mixed dyslipidemia was higher in patients with poor glycemic control and hypertension. The glycemic status of patients correlated with their fasting serum lipid profile. Dyslipidemia was associated mainly with male gender, poor glycemic control and hypertension. Conclusions Atherogenic dyslipidemia is associated mainly with male gender, poor glycemic control and hypertension. It is highly prevalent in Nepalese patients with type 2 diabetes. Urgent lifestyle modification, sustained glycemic control and aggressive lipid lowering treatment plans are necessary to minimize the future risk of cardiovascular disease in this population