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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.

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

The realization of high recognition rates of degraded documents such as palm leaf manuscripts primarily relies on document enhancement. Advancement of deep learning models in the process of document enhancement plays a major role among non-deep learning models or thresholding methods. Preparation of readily available ground truth data for creation of deep learning models is of paramount importance as it is highly time consuming task. The ground truth dataset preparation involves greater complexities as ancient documents are affected with degradations such as fungi, humidity, uneven illumination, discoloration, holes, cracks, and other damages. We propose a Handwritten Malayalam Palm Leaf Manuscript Dataset (HMPLMD) and its ground truth data aspiring for advancements in the field of palm leaf image analysis. We employ the palm leaf manuscripts of Kambaramayanam and Jathakas for the sake of experimentations. The proposed ground truth samples of degraded palm leaves plays a crucial role in creation of specialized deep/transfer learning models to handle challenges related to binarization.

The proposed research aims to restore deteriorated text sections that are affected by stain markings, ink seepages and document ageing in ancient document photographs, as these challenges confront document enhancement. A tri-level semi-adaptive thresholding technique is developed in this paper to overcome the issues. The primary focus, however, is on removing deteriorations that obscure text sections. The proposed algorithm includes three levels of degradation removal as well as pre- and post-enhancement processes. In level-wise degradation removal, a global thresholding approach is used, whereas, pseudo-colouring uses local thresholding procedures. Experiments on palm leaf and DIBCO document photos reveal a decent performance in removing ink/oil stains whilst retaining obscured text sections. In DIBCO and palm leaf datasets, our system also showed its efficacy in removing common deteriorations such as uneven illumination, show throughs, discolouration and writing marks. The proposed technique directly correlates to other thresholding-based benchmark techniques producing average F-measure and precision of 65.73 and 93% towards DIBCO datasets and 55.24 and 94% towards palm leaf datasets. Subjective analysis shows the robustness of proposed model towards the removal of stains degradations with a qualitative score of 3 towards 45% of samples indicating degradation removal with fairly readable text. Highlights This work presents a semi-adaptive binarization technique for ancient image enhancement. Main focus of this work is to restore obscured text sections. Multi-level thresholding approach is used for the removal of degradations. Gradient of the original image is used in the computation of reference image to detect deteriorated text sections. Pseudo-colouring and post-enhancement process finally transform to the enhanced image. DIBCO and palm leaf document samples are used for experimentations.

The realization of high recognition rates of degraded documents such as palm leaf manuscripts primarily relies on document enhancement. Advancement of deep learning models in the process of document enhancement plays a major role among non-deep learning models or thresholding methods. Preparation of readily available ground truth data for creation of deep learning models is of paramount importance as it is highly time consuming task. The ground truth dataset preparation involves greater complexities as ancient documents are affected with degradations such as fungi, humidity, uneven illumination, discoloration, holes, cracks, and other damages. We propose a Handwritten Malayalam Palm Leaf Manuscript Dataset (HMPLMD) and its ground truth data aspiring for advancements in the field of palm leaf image analysis. We employ the palm leaf manuscripts of Kambaramayanam and Jathakas for the sake of experimentations. The proposed ground truth samples of degraded palm leaves plays a crucial role in creation of specialized deep/transfer learning models to handle challenges related to binarization., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article., (© 2023 The Author(s).)