AMDPWE: Alphonso Mango Dataset for Precision Weight Estimation

Alphonso Mango (Mangifera indica L.), is popularly known as king of mangoes in India. India is one of the leading countries in mango production. Automatic visual inspection systems for quality assessment using weight are intelligent interventions designed to evaluate fruit maturity based on various parameters. Automated systems utilize a combination of image analysis, computer vision, and artificial intelligence algorithms to estimate the weight of fruits precisely. One of the crucial quality parameters is weight, which measures the fruit's overall mass and potential quality. Integration of precision weighing mechanisms in fruit quality estimation leads to a quick and accurate method of measuring fruit weight in the marketplace. Furthermore, the fruit's demand in the market is directly connected to its size as it influences consumer preferences. Automatic precision weight estimation systems equipped with intelligent high-resolution assists in ensuring consistency in size across batches of fruits. The dataset samples consist of images of 71 Alphonso cultivars of mango fruit. The fruit is collected from the College of Horticulture Yalachahalli, Mysuru, India. The fruits were harvested in April/May 2022. The digital images of these fruits are captured using the acquisition setup with a controlled environment. Each image has a resolution of 2048×1536. The images include two orientations of each sample. The physical parameters such as the weight, fruit diameter, and width across the shoulder are also maintained. The digital images undergo pre-processing, and further, the vision-based features such as area, convex area, and minor axis for both orientations are captured.

DIMPSAR: Dataset for Indian medicinal plant species analysis and recognition

Mobile-captured images of medicinal plants are widely used in various research investigations. Machine vision-based tasks such as the identification of plant species types for intelligent imaging device applications take a significant part in it. Botanists, farmers and researchers can reliably identify medicinal plants with the help of images captured using smartphones.  Mobile captured images can be used for quality control to make sure that the right plant species are being used in pharmaceutical products. In the field of education, pictures of medicinal plants and their usage can be used to educate learners, medical professionals, and the general public. Further, various research investigations in the area of chemistry, pharmacology, the therapeutic potential of medicinal plants, images can be employed. In this paper, we contribute a dataset of Indian medicinal plant species. The dataset is collected from different regions of Karnataka and Kerala. Datasets include characteristics such as multiple resolutions, varying illuminations, varying backgrounds, and seasons in the year. The datasets consist of 5900 images of forty plant species and single leaf images of eighty plant species consisting of 6900 samples obtained from real-time conditions using smartphones. The datasets contributed would be useful to researchers to investigate on development of algorithmic models based on image processing, machine learning, and deep learning concepts to educate about medicinal plants. The dataset can be accessed by anybody, without charge, at DOI:10.17632/748f8jkphb.2, 10.17632/748f8jkphb.3.

An orientation independent vision based weight estimation model for Alphonso mangoes

One of the most essential factors in classifying and qualitatively evaluating mangoes for various industrial uses is weight. To meet grading requirements during industrial processing, this paper presents an orientation-independent weight estimation method for the mango cultivar “Alphonso.” In this study, size and geometry are considered as key variables in estimating weight. Based on the visual fruit geometry, generalized hand-crafted local and global features, and conventional features are calculated and subjected to the proposed feature selection methodology for optimal feature identification. The optimal features are employed in regression analysis to estimate the predicted weight. Four regression models –MLR, Linear SVR, RBF SVR, and polynomial SVR—are used during the experimental trials. A self-collected mango database with two orientations per sample is obtained using a CCD camera. Three different weight estimation techniques are used in the analysis concerning orientation 1, orientation 2, and combining both orientations. The SVR RBF kernel yields a higher correlation between predicted and actual weights, and experiments demonstrate that orientation 1 is symmetric to orientation 2. By exhibiting a correlation coefficient of R2 = 0.99 with SVR-RBF for weight estimation using both orientations as well as individual orientations, it is observed that the correlation between predicted and estimated weights is nearly identical

Adaptive dewarping of severely warped camera-captured document images based on document map generation

Automated dewarping of camera-captured handwritten documents is a challenging research problem in Computer Vision and Pattern Recognition. Most available systems assume the shape of the camera-captured image boundaries to be anywhere between trapezoidal and octahedral, with linear distortion in areas between the boundaries for dewarping. The majority of the state-of-the-art applications successfully dewarp the simple-to-medium range geometrical distortions with partial selection of control points by a user. The proposed work implements a fully automated technique for control point detection from simple-to-complex geometrical distortions in camera-captured document images. The input image is subject to preprocessing, corner point detection, document map generation, and rendering of the de-warped document image. The proposed algorithm has been tested on five different camera-captured document datasets (one internal and four external publicly available) consisting of 958 images. Both quantitative and qualitative evaluations have been performed to test the efficacy of the proposed system. On the quantitative front, an Intersection Over Union (IoU) score of 0.92, 0.88, and 0.80 for document map generation for low-, medium-, and high-complexity datasets, respectively. Additionally, accuracies of the recognized texts, obtained from a market leading OCR engine, are utilized for quantitative comparative analysis on document images before and after the proposed enhancement. Finally, the qualitative analysis visually establishes the system's reliability by demonstrating improved readability even for severely distorted image samples.

A simple and efficient technique for leaf extraction in complex backgrounds of low resolution mobile photographed images

Low resolution mobile photographed images pose a complex set of research challenges as compared to non-mobile captured images, which really is a significant issue these days. For non-mobile captured and high-resolution photos, current plant recognition systems are the best solution providers. This study proposes the identification and extraction of leaf regions from complex backgrounds to meet the automatic recognition needs of a variety of mobile phone users. Additionally multiple factors complicate the leaf region extraction from complex backgrounds such as varying background patterns, clutters, varying leaf shape/size and varying illumination due to volatile weather conditions. In this paper, a simple and efficient method for leaf extraction from complex background of mobile photographed low resolution images is proposed based on color channel thresholding and morphological operations. A self-built database of 5000 mobile photographed images in realistic environments is adapted for experimentations. Experiments were conducted on various resolution categories, and it was discovered that the proposed model has an average dice similarity measure of 99.5 percent for successful extraction of the leaf region in 13MP mobile photographed images. Furthermore, our comparative investigation reveals that the suggested model outperforms both traditional and state-of-the-art techniques.

Chronological age assessment based on wrist radiograph processing – Some novel approaches

Assessing the age of an individual via bones serves as a technique in determination of individual skills. In this work, the assessment of chronological age for varying age groups of individuals is carried out using left hand wrist radiographs. The datasets employed for experimentation are preprocessed and extracted using an automated segmentation technique using bit plane level data of radiograph images. The flow of proposed work is comprised of three stages, in stage 1 preprocessing is carried out, classification of preprocessed radiographs are classified into male and female samples using convolution kernels based deep neural net. Further, distance features are extracted from the origin of carpal bones to tip of extracted phalangeal regions in the classified outcomes from stage 2 using imtool image analyzer. Finally, classification of distance features is performed using Support Vector Machines with Gaussian Kernel (SVM-GK) to label the radiographs into ages from 1 to 17. The experimentation is performed on the datasets of Pediatric Bone Age challenge of Radiological Society of North America (RSNA) of about 12000 images of 1–17 year age groups. The convergence between actual and clinically validated chronological age is also tested with Gaussian process regression model (GPRM) along with SVM. A very minimal loss of about 4.7% is occurred during classification using deep neural network. The classification accuracy is found to be 76.8% and 88.1% and 0.75 and 1.41 RMSE with respect to GPRM and SVM-GK.

Diversity of global rice markets and the science required for consumer-targeted rice breeding

With the ever-increasing global demand for high quality rice in both local production regions and with Western consumers, we have a strong desire to understand better the importance of the different traits that make up the quality of the rice grain and obtain a full picture of rice quality demographics. Rice is by no means a 'one size fits all' crop. Regional preferences are not only striking, they drive the market and hence are of major economic importance in any rice breeding / improvement strategy. In this analysis, we have engaged local experts across the world to perform a full assessment of all the major rice quality trait characteristics and importantly, to determine how these are combined in the most preferred varieties for each of their regions. Physical as well as biochemical characteristics have been monitored and this has resulted in the identification of no less than 18 quality trait combinations. This complexity immediately reveals the extent of the specificity of consumer preference. Nevertheless, further assessment of these combinations at the variety level reveals that several groups still comprise varieties which consumers can readily identify as being different. This emphasises the shortcomings in the current tools we have available to assess rice quality and raises the issue of how we might correct for this in the future. Only with additional tools and research will we be able to define directed strategies for rice breeding which are able to combine important agronomic features with the demands of local consumers for specific quality attributes and hence, design new, improved crop varieties which will be awarded success in the global market.

Different isoforms of starch-synthesizing enzymes controlling amylose and amylopectin content in rice (Oryza sativa L.)

Starch, composed of amylose and amylopectin, greatly influences rice cooking and textural quality, which in turn is controlled by various isoforms of several enzymes. Activity of one or more isoforms of starch-synthesizing enzymes results in various forms of starch structure based on the amylopectin chain length and average external, internal and core chain length distribution and hence results in varying physicochemical and cooking quality. Since the synthesis of starch is highly complex, it is crucial but essential to understand its biosynthetic pathway, starch structure and effects on the physicochemical properties that control eating and cooking quality, and alongside conduct research on gene/QTL mapping for use in marker-assisted selection (MAS) with a view to improve and select cultivars with most desirable range and class of rice starch properties. This article presents the updates on current understanding of the coordination among various enzymes/isoforms towards rice starch synthesis in endosperm and their effect on rice grain physicochemical, cooking and eating qualities. The efforts in identifying regions responsible for these enzymes by mapping the gene/QTLs have provided a glimpse on their association with physicochemical and cooking properties of rice and, hence, improvement is possible by modifying the allelic pattern, resulting in down or nil regulation of a particular enzyme. The clear understanding of the tissue specific coordination between enzyme isoforms and their subsequent effect in controlling eating and cooking properties will enhance the chances to manipulate them for getting desired range of amylose content (AC) and gelatinization temperature (GT) in improved cultivars through combining desired alleles through MAS.

QTL analysis for grain quality traits in 2 BC2F2 populations derived from crosses between Oryza sativa cv Swarna and 2 accessions of O. nivara

The appearance and cooking quality of rice determine its acceptability and price to a large extent. Quantitative trait loci (QTLs) for 12 grain quality traits were mapped in 2 mapping populations derived from Oryza sativa cv Swarna × O. nivara. The BC(2)F(2) population of the cross Swarna × O. nivara IRGC81848 (population 1) was evaluated during 2005 and that from Swarna × O. nivara IRGC81832 (population 2) was evaluated during 2006. Linkage maps were constructed using 100 simple sequence repeat (SSR) markers in population 1 and 75 SSR markers in population 2. In all, 21 QTLs were identified in population 1 (43% from O. nivara) and 37 in population 2 (38% QTLs from O. nivara). The location of O. nivara-derived QTLs mp1.2 for milling percent, kw6.1 for kernel width, and klac12.1 for kernel length after cooking coincided in the 2 populations and appear to be useful for Marker Assisted Selection (MAS). Four QTLs for milling percent, 1 QTL each for amylose content, water uptake, elongation ratio, 2 QTLs for kernel width, and 3 QTLs for gel consistency, each explained more than 20% phenotypic variance. Three QTL clusters for grain quality traits were close to the genes/QTLs for shattering and seed dormancy. QTLs for 4 quality traits were associated with 5 of the 7 major yield QTLs reported in the same 2 mapping populations. Useful introgression lines have been developed for several agronomic traits. It emerges that 40% O. nivara alleles were trait enhancing in both populations, and QTLs for grain quality overlapped with yield meta-QTLs and QTLs for dormancy and seed shattering.

Identification and fine-mapping of Xa33, a novel gene for resistance to Xanthomonas oryzae pv. oryzae

Broadening of the genetic base for identification and transfer of genes for resistance to insect pests and diseases from wild relatives of rice is an important strategy in resistance breeding programs across the world. An accession of Oryza nivara, International Rice Germplasm Collection (IRGC) accession number 105710, was identified to exhibit high level and broad-spectrum resistance to Xanthomonas oryzae pv. oryzae. In order to study the genetics of resistance and to tag and map the resistance gene or genes present in IRGC 105710, it was crossed with the bacterial blight (BB)-susceptible varieties 'TN1' and 'Samba Mahsuri' (SM) and then backcrossed to generate backcross mapping populations. Analysis of these populations and their progeny testing revealed that a single dominant gene controls resistance in IRGC 105710. The BC(1)F(2) population derived from the cross IRGC 105710/TN1//TN1 was screened with a set of 72 polymorphic simple-sequence repeat (SSR) markers distributed across the rice genome and the resistance gene was coarse mapped on chromosome 7 between the SSR markers RM5711 and RM6728 at a genetic distance of 17.0 and 19.3 centimorgans (cM), respectively. After analysis involving 49 SSR markers located between the genomic interval spanned by RM5711 and RM6728, and BC(2)F(2) population consisting of 2,011 individuals derived from the cross IRGC 105710/TN1//TN1, the gene was fine mapped between two SSR markers (RMWR7.1 and RMWR7.6) located at a genetic distance of 0.9 and 1.2 cM, respectively, from the gene and flanking it. The linkage distances were validated in a BC(1)F(2) mapping population derived from the cross IRGC 105710/SM//2 × SM. The BB resistance gene present in the O. nivara accession was identified to be novel based on its unique map location on chromosome 7 and wider spectrum of BB resistance; this gene has been named Xa33. The genomic region between the two closely flanking SSR markers was in silico analyzed for putatively expressed candidate genes. In total, eight genes were identified in the region and a putative gene encoding serinethreonine kinase appears to be a candidate for the Xa33 gene.

Introduction of bacterial blight resistance into Triguna, a high yielding, mid-early duration rice variety

Bacterial blight (BB) is a serious disease of rice in India. We have used molecular marker-assisted selection in a backcross breeding program to introgress three genes (Xa21, xa13, and xa5) for BB resistance into Triguna, a mid-early duration, high yielding rice variety that is susceptible to BB. At each generation in the backcross program, molecular markers were used to select plants possessing these resistance genes and to select plants that have maximum contribution from the Triguna genome. A selected BC3F1 plant was selfed to generate homozygous BC(3)F(2) plants with different combinations of BB resistance genes. Plants containing the two-gene combination, Xa21 and xa13, were found to exhibit excellent resistance against BB. Single plant selections for superior agronomic characteristics were performed on the progeny of these plants, from BC(3)F(3) generation onwards. The selected plants were subjected to yield trials at the BC(3)F(8) generation and were found to have a significant yield advantage over Triguna. The newly developed lines are being entered into national multi-location field trials. This work represents a successful example of the application of molecular marker-assisted selection for BB resistance breeding in rice.

Genetic and molecular basis of fragrance in rice

Fragrance or aroma in rice is considered as a special trait with huge economic importance that determines the premium price in global trade. With the availability of molecular maps and genome sequences, a major gene for fragrance (badh2) was identified on chromosome 8. An 8-bp deletion in the exon 7 of this gene was reported to result in truncation of betaine aldehyde dehydrogenase enzyme whose loss-of-function lead to the accumulation of a major aromatic compound, 2-acetyl 1-pyrroline (2AP) in fragrant rice. However, several studies have reported exceptions to this mutation and indicated the involvement of other genetic loci in controlling fragrance trait. These studies emphasize the need to characterize the fragrance and its underlying factors in a wide range of genetic resources available for this trait. This review summarizes the new insights gained on the genetic and molecular understanding of fragrance in rice.