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Satheesha, K. M., K. S. Rajanna, and Prasad K. Krishna. "A Review of the Literature on Arecanut Sorting and Grading Using Computer Vision and Image Processing." International Journal of Applied Engineering and Management Letters (IJAEML) 7, no. 2 (2023): 50–67. https://doi.org/10.5281/zenodo.7878092.
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<strong>Background/Purpose: </strong><em>These days, the involvement of computer science in agriculture and food science is expanding. Classification and fault identification of diverse products employ a variety of Artificial Intelligence (AI), soft computing approaches, and methodologies, which contribute to higher-quality products for consumers. The position of Arecanuts in the international and Indian markets, as well as the application of computer vision and image processing to a system for categorizing and grading Arecanuts, are the main topics of this article.</em> <strong>Objective: </strong><em>The development of a system for the automated categorization of Arecanut using images is limited by difficulties. To assess the value of computer vision application for Arecanut, it is critical to taken as account the traditional and economic significance of Arecanut.</em> <strong>Design/Methodology/Approach</strong>: <em>Several types of Arecanut are prone to great variation in color, texture, and form depending on the category and the area in which they are cultivated. Arecanuts are processed utilizing a variety of techniques, with an emphasis on the finished product's exterior. Here, the color, size, and texture of Arecanut are used to construct a classification or grading system.</em> <strong>Findings/Result: </strong><em>With reference to the cited significant work that has been done on other fruits as well as Arecanuts from the standpoint of computer vision. This article provided a thorough introduction to Arecanuts, computer vision, and the uses and benefits of vision-aided technologies in the grading of Arecanuts and categorization.</em> <strong>Result Limitations/Implications: </strong><em>This review is based on the detection and classification of the Arecanuts done using computer vision and AI techniques.</em> <strong>Originality Value: </strong><em>Several inline resources including review papers on Arecanut, research articles, technical books, and website resources.</em> <strong>Paper Type: </strong><em>Literature</em> <em>Review paper on smart auto Arecanut Sorting and Grading of Arecanut using Computer Vision and Image Processing</em>
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Shedthi B, Shabari, Madappa Siddappa, Surendra Shetty, and Vidyasagar Shetty. "Classification of arecanut using machine learning techniques." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1914. http://dx.doi.org/10.11591/ijece.v13i2.pp1914-1921.
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In agricultural domain research, image processing and machine learning techniques play an important role. This paper provides a unique solution for classifying the good and defective arecanuts based on their color, texture, and density value. In the market different varieties of arecanut are available. Usually, qualitative sorting is done manually, and this can be replaced by applying machine vision techniques to grade the arecanut. Classification of arecanut based on quality is done using various machine learning techniques and it is observed that artificial neural networks give good results compared to other classifiers like logistic regression, <em>k</em>-nearest neighbor, naive Bayes classifiers, and support vector machine. A unique density feature is considered here for better classification. The result of classifiers without considering the density feature is compared with respect to the density feature and it is observed that artificial neural networks work better than the others. The proposed method works effectively for classifying arecanut with an accuracy of 98.8%.
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Shabari, Shedthi Billadi, Siddappa Madappa, Shetty Surendra, and Shetty Vidyasagar. "Classification of arecanut using machine learning techniques." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1914–21. https://doi.org/10.11591/ijece.v13i2.pp1914-1921.
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In agricultural domain research, image processing and machine learning techniques play an important role. This paper provides a unique solution for classifying the good and defective arecanuts based on their color, texture, and density value. In the market different varieties of arecanut are available. Usually, qualitative sorting is done manually, and this can be replaced by applying machine vision techniques to grade the arecanut. Classification of arecanut based on quality is done using various machine learning techniques and it is observed that artificial neural networks give good results compared to other classifiers like logistic regression, k-nearest neighbor, naive Bayes classifiers, and support vector machine. A unique density feature is considered here for better classification. The result of classifiers without considering the density feature is compared with respect to the density feature and it is observed that artificial neural networks work better than the others. The proposed method works effectively for classifying arecanut with an accuracy of 98.8%.
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Dhanesha R., Et al. "Segmentation and Classification of Arecanut Bunches before harvesting." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 3953–62. http://dx.doi.org/10.17762/ijritcc.v11i9.9736.
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In the agriculture sector, arecanuts are an extremely valuable crop. The price of an arecanut depends on its stage of ripeness. As a result of a lack of expertise in judging the maturity level of arecanut bunches before harvest, farmers often lose profit. Precision agricultural techniques based on image processing and computer vision have recently assisted farmers in determining crop maturity quality. Precision agricultural techniques based on image processing and computer vision have recently assisted farmers in determining crop maturity quality. Therefore, accuracy in segmenting arecanut bunches is vital for automated maturity level identification. In proposed work S-channel, Cr-channel and Pr-channel of HSV, YCbCr and YPbPr respectively color models are used to segment arecanut bunches. Three color features (i.e., mean of an arecanut bunch image on red, green, and blue bands), and two texture features (i.e, correlation, and entropy) were used in classification procedure. A random forest classifier was employed to classify maturity levels of arecanut bunch. This experiment uses a dataset of 1017 images of arecanut bunches to assess the segmentation performance of each color model. As a result of the experiment, it has been concluded that the S-channel of the HSV color model was effective in segmenting arecanut bunches from input images. The proposed methodology effectively classifies arecanut bunch maturity levels with an accuracy of 87.80%.
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Hegde, Ajit, Vijaya Shetty Sadanand, Chinmay Ganapati Hegde, Krishnamurthy Manjunath Naik, and Kanaad Deepak Shastri. "Identification and categorization of diseases in arecanut: a machine learning approach." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 3 (2023): 1803. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1803-1810.
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Arecanut is one of the prominent commercial crops that are grown worldwide for traditional medicines, furniture, cosmetics, food, veterinary preparations, and textile industries. It experiences a variety of diseases during its existence, from the bottom to the tip. The conventional method for detection of diseases is through visual inspection and it is also necessary to have properly designed laboratories to check these harvests. It is a time consuming and tedious task to inspect these crops across wide acres of plantations. The proposed system has been developed that uses convolutional neural network (CNN) to identify and categorize diseases in arecanuts, trunks and leaves also suggesting effective preventative measures. Proprietary dataset consists of 1,100 photos of healthy and diseased arecas. The ratio between the train and test data is 80:20. Binary cross entropy is employed as the loss function for model construction, with accuracy serving as the metrics and Adam serving as the optimizing function. In identification and categorization of arecanut diseases, the suggested approach was shown to be efficient with 93.05% accuracy.
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B, Chethan. "Arecanut Disease Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41024.
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The tropical crop arecanut, sometimes referred to as betel nut, is primarily farmed in India. In terms of arecanut production and consumption, the nation ranks second in the world. The areca nut plant is vulnerable to numerous diseases that impact its roots, stem, leaves, and fruits throughout its life cycle. While some of these illnesses can be seen with the naked eye, others cannot. These illnesses are brought on by abrupt changes in temperature and other meteorological factors; early disease identification is crucial. In order to minimize losses for farmers, this work focuses on early and precise disease diagnosis. Using convolutional neural networks, we developed a system that assists in identifying arecanut, leaf, and trunk ailments and offers treatments. A Convolutional Neural Network (CNN) is a Deep Learning method that uses an image as input, gives different items in the image learnable weights and biases, and then uses the results to determine which objects are different. We took a dataset with 620 photos of arecanuts in both good and unhealthy conditions in order to train and evaluate the CNN model. An 80:20 ratio is used to separate the test and train data. Adam serves as the optimizer function, accuracy serves as a measure, and categorical cross-entropy serves as the loss function for the model compilation. To attain high validation and test accuracy with little loss, the model is trained over a total of 5 epochs. It was discovered that the suggested method was successful and 88.46 percent accurate in detecting arecanut illness. Diseases frequently seen in areca trees include Mahali Disease (Koleroga), Bud Rot Disorder, Stem Exudation, Yellow Leaf Blotch,Yellow Disease, which arises from persistent rainfall and climate alterations, these ailments need to be managed in the initial phase of infection; otherwise, it could lead to difficulties in oversight in the concluding phase that could result in detriment to the latter. To prevent this, we can utilize Machine Learning for disease detection. We will identify yellow leaf spot, stem bleeding, and Mahali disease (KoleRoga) in this project and provide treatments for the conditions we find. Key words: Convolution Neural Networks, Arecanut, and Machine Learning
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Chandrashekhara, H., and M. Suresha. "Classification of Healthy and Diseased Arecanuts using SVM Classifier." International Journal of Computer Sciences and Engineering 7, no. 2 (2019): 544–48. http://dx.doi.org/10.26438/ijcse/v7i2.544548.
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Puneeth, B. R., and P. S. Nethravathi. "A Literature Review of the Detection and Categorization of various Arecanut Diseases using Image Processing and Machine Learning Approaches." International Journal of Applied Engineering and Management Letters (IJAEML) 5, no. 2 (2021): 183–204. https://doi.org/10.5281/zenodo.5773853.
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<strong>Background/Purpose: </strong><em>Every scholarly research project starts with a survey of the literature, which acts as a springboard for new ideas. The purpose of this literature review is to become familiar with the study domain and to assess the </em><em>work's credibility. It also improves with the subject's integration and summary. This article briefly discusses the detection of disease and classification to achieve the objectives of the study.</em> <strong>Objective:</strong> <em>The main objective of this literature survey is to explore the different techniques applied to identify and classify the various diseases on arecanut. This paper also recommends the methodology and techniques that can be used to achieve the objectives of the study.</em> <strong>Design</strong><strong>/</strong><strong>Methodology</strong><strong>/</strong><strong>Approach</strong>: <em>Multiple data sources, such as journals, conference proceedings, books, and research papers published in reputable journals, were used to compile the essential literature on the chosen topic and collect information from the arecanuts research centre and many farmers in the south Canara and Udupi districts, before narrowing down the literature that is relevant to the research work. The shortlisted literature was carefully assessed by reading each paper and taking notes as appropriate. The information gathered is then examined to identify the potential gap in the study.</em> <strong>Findings</strong><strong>/</strong><strong>Result</strong>: <em>Based on the analysis of the papers reviewed, discussion with farmers and research center officers, it is observed that, not much work is carried out in the field of disease identification and classification on arecanut using machine learning techniques. This survey paper recommends techniques and the methodology that can be applied to identify and classify the diseases in arecanut and to classify them in to healthy and unhealthy.</em> <strong>Research limitations</strong><strong>/</strong><strong>implications</strong>: <em>The literature review mentioned in this paper are detection and classification of different diseases in arecanut.</em> <strong>Originality</strong><strong>/</strong><strong>Value</strong>: <em>This paper focuses on various online research journals, conference papers, technical books, and web articles.</em> <strong>Paper Type</strong>: <em>Literature review paper on techniques and methods used to achieve the objectives.</em>
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Sabu, Kiran M., and T. K. Manoj Kumar. "Predictive analytics in Agriculture: Forecasting prices of Arecanuts in Kerala." Procedia Computer Science 171 (2020): 699–708. http://dx.doi.org/10.1016/j.procs.2020.04.076.
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Salunke, AS, and SS Honnungar. "Development of a true density-based automated quality grading device for unboiled arecanut kernels." African Journal of Food, Agriculture, Nutrition and Development 24, no. 8 (2024): 24298–318. http://dx.doi.org/10.18697/ajfand.133.24400.
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The automated sorting of arecanut kernels is a significant challenge that has not been effectively addressed thus far. Scientific grading techniques are necessary given the paradigm change toward investigating alternative uses for arecanuts in industry and the medical field. This research work emphasizes the relatively unexplored aspect of the post-harvest process; quality grading of kernels based on physical properties. It aimed to develop a novel approach for classifying unboiled (Chali) arecanut kernels cultivated in Goa, India based on their true density, using a combination of mechanical and visual techniques. The study explored the potential of true density as a quality indicator for real-time grading of the kernels. To achieve this, automated grading equipment was devised, utilizing a load cell to measure the kernel's mass and the ellipsoid approximation method to estimate its volume. A machine vision system captured the top and side images of the kernels to measure their volume. Python programs were created to enable image acquisition, processing, object detection, measurement and kernel segregation. Real-time kernel classification was accomplished by establishing serial data communication between the Python code and the Arduino board. The kernel segregation process was facilitated by servomechanism and a stepper motor. The kernels were classified into acceptable and non-acceptable categories based on a threshold value of true density. The research successfully established a method that utilizes the physical attributes of arecanut kernels as parameters for quality grading. However, the study encountered challenges with the density measurements, as the paired t-test results revealed significant differences between the kernel true density measured by the device and the true density estimated using the weighing scale-water displacement method, indicating a percentage error of 13.2%. Addressing these challenges would lead to more accurate density calculations, thereby enhancing the overall effectiveness of the kernel classification process. Furthermore, the technique allowed for the offline estimation of the kernels' porosity, which was found to be 45.3%. In future research, the integration of density and porosity measurement systems could be explored for real-time quality evaluation based on porosity, offering potential opportunities for further enhancement and optimization of the grading process. The technology could be further applied to other types of nuts and agricultural products, thereby overcoming the limitations of color-based sorting using image processing. Key words: Quality grading, True density, Machine vision, Arecanut kernel, Python
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Danti, Ajit, and Suresha. "Segmentation and Classification of Raw Arecanuts Based on Three Sigma Control Limits." Procedia Technology 4 (2012): 215–19. http://dx.doi.org/10.1016/j.protcy.2012.05.032.
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R. Gurumurthy, B., H. C. Swathi, J. Sahana, and S. P. Nataraj. "Studies on Variation in Arecoline Content of Arecanuts Collected from Different Parts of Karnataka." Biosciences, Biotechnology Research Asia 14, no. 2 (2017): 691–95. http://dx.doi.org/10.13005/bbra/2496.
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ABSTRACT: Arecoline is a nicotinic acid-based alkaloid found in the areca nut, the fruit of the areca palm (Areca catechu). It is an oily liquid that is soluble in water, alcohols, and ether. HPLC method is simple and rapid for determination of arecoline content in areca nut. Areca samples were collected from Shimoga, Davanagere, Chikkamagalur, Chitradurga, Dakshina kannada and Udupi districts of Karnataka, India. The collected areca samples were powdered and arecoline is extracted from samples collected from different hoblies of the districts. The extraction method was optimized to obtain pure arecoline before analysis to separate any interference in order to maximize the specificity and sensitivity of the method. The regionwise arecoline content has been compared. The concentration of arecoline varied from area to area depending on environmental factors, differential processing methods, age of the plantations, varietal differences etc.
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M, Suresha, and Ajit Danti. "Construction of Co-occurrence Matrix using Gabor Wavelets for Classification of Arecanuts by Decision Trees." International Journal of Applied Information Systems 4, no. 6 (2012): 33–39. http://dx.doi.org/10.5120/ijais12-450775.
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Chatra, L. "TO EVALUATE AND COMPARE THE COPPER CONTENT IN ARECANUTS SPRAYED WITH AND WITHOUT COPPER-CONTAINING FUNGICIDE BY ATOMIC ABSORPTION SPECTROSCOPY." Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology 132, no. 1 (2021): e14. http://dx.doi.org/10.1016/j.oooo.2021.03.063.
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Miftahorrachman, NFN, Sukmawati Mawardi, and Ismail Maskromo. "Korelasi dan Analisis Lintas Antara Karakter Agronomi dengan Hasil pada Pinang Emas (Areca catechu L.) [Correlation and Path Analysis Between Agronomy Characters of Pinang Emas (Areca catechu L.) with Yield ]." Buletin Palma 20, no. 1 (2019): 1. http://dx.doi.org/10.21082/bp.v20n1.2019.1-9.
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<pre>Pinang Emas is a new high yielding variety of arecanut, is resulted from positive mass selection of arecanuts population in Kota Kotamobagu, North Sulawesi Province. The study was purposing to estimate the correlation between vegetative, generative and fruit component characters to yield of Pinang Emas. The research was conducted in The Kayuwatu Experimental Garden, Indonesia Coconut Palm Research Institue, North Sulwesi, from January to December 2018. Path analysis of 21 characters using formula of Singh and Chaudary. The results of simple correlation analysis produce 25 relationships, most of which are relationships among the fruit component characters and there is no correlation with the number of fruits per bunch (JBT). The results of a simple correlation analysis resulted in 25 relationships, most of which were relationships between fruit component characters and there was no correlation with the number of fruits per bunch (JBT). Path analysis of six characters of fruit component showed only character of polar length of unhusked fruit (PPBTS) has direct effect to weight of fruit with value of r= 0.56 and indirect effect of BBBTS through PPBTS caharacter. The benefit of this research is that the character of PPBTS can be used as a selection criterion for the improvement of the production of Pinang Emas.</pre><p align="center"><strong><br /></strong></p><p align="center"><strong><br /></strong></p><p align="center"><strong>ABSTRAK</strong></p><p>Pinang Emas merupakan varietas unggul baru pinang, sebagai hasil seleksi massa positif dari tetua yang berasal dari Kota Kotamobagu, Sulawesi Utara. Penelitian ini bertujuan untuk mengetahui korelasi antara karakter vegetatif, generatif dan komponen buah dengan produksi buah varietas Pinang Emas. Penelitian ini dilaksanakan di Kebun Percobaan Kayuwatu, Balai Penelitian Tanaman Palma, Sulawesi Utara, mulai bulan Januari sampai Desember 2018. Analisa sidik lintas 21 karakter vegetatif, generatif serta komponen buah terhadap karakter jumlah buah per tandan menggunakan rumus dari Singh dan Chaudary. Hasil analisis korelasi sederhana menghasilkan 25 hubungan, sebagian besar adalah hubungan antar karakter komponen buah dan tidak terdapat korelasi dengan jumlah buah per tandan (JBT). Hasil analisis sidik lintas tujuh karakter komponen buah, hanya karakter panjang polar buah tanpa sabut (PPBTS) yang berpengaruh langsung terhadap berat buah utuh (BBU) dengan nilai r=0.56, dan pengaruh tidak langsung karakter BBBPTS melalui karakter PPBTS. Manfaat penelitian ini adalah karakter PPBTS dapat dijadikan kriteria seleksi untuk perbaikan produksi Pinang Emas.</p><p align="center"> </p><pre> </pre>
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Priyaa, R. Bharathi, S. Nazreen Hassan, M. Nirmala Devi, R. Pangayar Selvi, and S. Selvanayaki. "Challenges in Adopting Value Addition Technologies in Arecanut." Asian Journal of Agricultural Extension, Economics & Sociology 41, no. 9 (2023): 819–23. http://dx.doi.org/10.9734/ajaees/2023/v41i92108.
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The rise of opportunity for stakeholders in the arecanut sector to explore the potential of value-added arecanut products. The arecanut farmers are facing a lack of training on arecanut practices, the improper post-harvest practices deteriorate the quality of nuts that cause the nuts to fetch lower price in the market. They are also not aware of the opportunities for value added arecanut. The knowledge level of post- harvest practices of arecanut is lacking among arecanut growers. The arecanut farmers sell the raw nuts to the pre-harvest contractors without knowing the opportunities available in arecanut value addition. The contractors in turn they carry out the value addition and sold in the market. The price of the nut is mainly decided by its quality which correlates to post-harvest practices and value addition that plays a major role in arecanut value.The study investigated the challenges encountered by arecanut farmers in adopting value addition technology.The datas were collected among arecanut farmers related to the challenges they face in adopting value addition technology. The ex-post facto research design was employed; several kinds of constraints were gathered through a literature study and expert opinion. They were given in an interview schedule for the farmers to rank accordingly. The acquired data were analyzed, and ranks were assigned based on Rank-Based Quotient percentage.The major challenges faced by the arecanut farmers were Lack of credit (85%), Inadequate knowledge of value addition technology (82.5%), No re-training facilities (81.675), Lack of equipment/facilities (78.33%), Lack of market (74.17%) and Contacting extension agents (56.67%).By overcoming these limitations, arecanut farmers can adopt value addition technology, leading to profit.
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Desai, Nagappa, K. R. Shreenivasa, M. S. Anitha, and M. H. Shankara. "Effect of Intercropping System of Vegetables on Yield and Economics of Arecanut Plantation." International Journal of Plant & Soil Science 35, no. 21 (2023): 1280–87. http://dx.doi.org/10.9734/ijpss/2023/v35i214107.
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The on farm trials were conducted to study the effect of intercropping system of vegetables on yield, economics and soil fertility status of Arecanut plantation during the year from 2015-16 to 2016-17 at various villages in Tiptur taluk of Tumkur district, Karnataka state. The three intercropping systems involved viz., Treatment T1- Arecanut as mono-cropping (farmers practice), Treatment T2 - Arecanut intercrops with vegetable cowpea and Treatment T3- Arecanut intercrops with vegetable french bean conducted at seven farmers field as replication. The 26 year old Arecanut plantation of Gubbi local variety planted with 2.7 m x 2.7 m spacing. The result reveals that the two year mean data of experiments were recorded. The maximum chali yield of Arecanut was recorded (12.53 q/ha/year) in Arecanut plantation intercrops with vegetable french bean, which is on far with Arecanut plantation (12.25 q/ha/year) intercrops with vegetable cowpea. Whereas, significantly minimum chali yield of arecanut was obtained in Arecanut as mono-cropping system in farmers practice. The cultivation of Arecanut intercrops with vegetable french bean was obtained highest net annual income (Rs. 2,56,832/ha) and B:C ratio (2.85) with additional income of the farmers and high market demand of vegetable beans as compared to cultivation of Arecanut plantation intercrops with vegetable cowpea at Rs. 2,29,083/ha with B:C ratio of 2.72 with less demand of vegetable cowpea at market, whereas, cultivation of Arecanut as monocropping system was recorded lowest net annual income (Rs.1,45,290/ha) with B:C ratio (2.29) and no additional income from arecanut mono-cropping system practices in farmers field.
 The soil samples were collected from arecanut plantation before initiated and after the experiment, analyzed the soil fertility status of Nitrogen, Phosphorus and Potash availability, pH and electrical conductivity (EC) in soil. Soil pH and organic matter content of samples were slightly alkaline to neutral and low to high respectively. The soil fertility status of Arecanut plantation intercrops with vegetable French beans was recorded numerical improved (P<0.05) in all three major nutrients over the pre-treatment observation in the soil, which is on far with soil fertility status of Arecanut plantation intercrops with vegetable cowpea, whereas, soil fertility status of NPK were found to lowest in Arecanut as mono- cropping system as farmers practices. This might be due to incorporated residue of French bean and cowpea biomass into soil, which fixes atmospheric nitrogen as results in improved of soil fertility status. Arecanut plantation intercrops with vegetable french bean were recorded higher net returns and sustainability of the soil fertility by cultivating more suitable cropping system to enhanced income of farmers.
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T, Nayana, and Dr T. D. Shashikala. "CLASSIFICATION OF ARECANUT USING DIGITAL IMAGE PROCESSING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 09 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25828.
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In agricultural domain research, image processing and machine learning techniques play an important role. One of India’s major cash crops is arecanut. A significant challenge in the field of agriculture is the grouping of arecanut. Arecanut categorization using image processing is an emerging field of research that aims to automate the process of grouping arecanut based on its color and shape using digital images. This paper presents a classification of Arecanut using Convolutional Neural Networks. General Terms Image Processing, Classification Keywords Arecanut classification, Machine learning, Convolutional Neural Networks, Deep learning.
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JAMANAL, HANUMANTAPPA, and C. MURTHY. "Constraints faced in production and marketing of arecanut in Karnataka." Journal of Farm Sciences 37, no. 01 (2024): 54–58. http://dx.doi.org/10.61475/jfs.2024.v37i1.13.
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The arecanut is one of the most important crops grown in Karnataka and the state’s area under arecanut cultivation has nearly doubled in the last 15 years. Shivamogga, Davanagere, Chikkamagaluru, Dakshina Kannada. Tumkur and Uttara Kannada are the major arecanut producing districts of Karnataka, the accounting for a sizable share of 60 per cent of the area and 65 per cent of arecanut production in the state. The random sampling method was used for selection of arecanut growers and four districts were selected namely Dakshina Kannada, Chikkamagaluru, Davanagere and Shivamogga. Each districts two talukas were selected based on highest area and production of arecanut, each talukas six villages were selected and four farmers from each village were chosen for the study. Thus a total of 192 arecanut farmers were selected. To analyse the problems faced by the market intermediaries five traders, five wholesalers, five pre harvest contractors and five retailers were selected from talukas from selected districts. The total marketing intermediaries were 160. Thus, the total sample size was 360. The majority of the farmers are facing problems in production mammalians pest attacking on arecanut bunch (73.56) in Davanagere district, high wage rates of labour in Chikkamagaluru and incidence of pest and diseases attack in Shivamogga and Dakshina Kannada districts. In case of arecanut marketing problems were lack of storage facilities, poor transport facility and price fluctuations. The need of present era is to increase the productivity of arecanut crop. This could be achieved by adopting improved production practice, high yield varieties and new technologies of crop
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M U, Likhitha, and Dr Geetha M. "AI BASED ARECANUT PLANT DISEASE CLASSIFICATION SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem36460.
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Arecanut, commonly known as betel nut, is a vital cash crop in many tropical regions, contributing significantly to the agricultural economy. However, like other crops, arecanut plants are susceptible to various diseases that can severely impact yield and quality. Early detection and accurate classification of these diseases are crucial for timely intervention and effective disease management. In this study, we propose an AI-based arecanut plant disease classification system that leverages deep learning techniques to automatically identify and classify different diseases affecting arecanut plants. Convolutional neural networks (CNNs) are employed for feature extraction and disease classification, with transfer learning techniques used to fine-tune pre-trained models on the specific task of arecanut disease recognition. Keywords: Arecanut, betel nut, plant disease classification, deep learning, transfer learning, agricultural AI, disease management. convolutional neural network (CNN).
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Patil, Kiran Kumar R. "Comparative economics of mechanical and manual dehusking of Arecanut." Indian Journal of Economics and Development 8 (December 9, 2020): 1–9. http://dx.doi.org/10.17485/ijed/v8.15.
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Objectives: This research was undertaken to examine the economic benefit of mechanization in dehusking arecanut. Methods/Statistical analysis: A sample consisting 15 each of pre-harvest contractors, arecanut farmers using machines of different capacities for dehusking arecanut were randomly selected from Davanagere district of Karnataka. The economic viability of mechanization was assessed under these situations using partial budgeting technique. Findings: The results of the study indicated that the cost of dehusking one quintal of arecanut using machine was ₹141 as compared to manual method (₹276) in case of pre-harvest contractors. The per quintal cost of dehusking of own arecanut produce came to Rs.78 as against manual method(Rs.277)in case of farmers who used the machine for dehusking of their own arecanut produce and rental purpose. Application/Improvements: The study suggested that marginal farmers should use two gear machine, small farmers four gear machine and large farmers six gear machine to economize dehusking their own farm produce and earning rental income. Keywords: Mechanization; Arecanut dehusking; economic evaluation; horticulture
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Karunakaran, N. "Dynamism in Area, Production and Productivity of Arecanut in Kerala." Artha - Journal of Social Sciences 15, no. 4 (2016): 51. http://dx.doi.org/10.12724/ajss.39.4.
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Arecanut is an important commercial crop in Kerala. It finds a place in all religious, social and cultural functions of the people of Kerala. Kerala accounts for 22.47 percentage of the area under arecanut in India contributing to 13.70 percentage of national production. During the past five decades, arecanut cultivation has underwent expansion in the area under cultivation associated with increase in production. The analysis of inter-district performance supports this finding. A comparison of the compound growth rates of arecanut productivity during the five periods reveals slight increase in the growth of arecanut productivity and supporting it. The period since the middle of 1980, with regard to area and production the coefficient of variation was higher as compared to 1960’s and 1970’s. A significant increase in production had occurred consequent to increase in area; however, the productivity of arecanut remains almost stagnant. Nonetheless, for the period since 1985, change in production was mainly due to yield effect. An analysis of the growth of output of arecanut crop in Kerala reveals that the growth is mainly monetary in nature rather than real growth.
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Hameed, A. A., and A. Najeeb. "Knowledge on Health Effects and Current Practice Toward Arecanut Use Among Secondary School Children Living in Malé City, Maldives." Journal of Global Oncology 4, Supplement 2 (2018): 20s. http://dx.doi.org/10.1200/jgo.18.35600.
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Background: The fourth most commonly used addictive substance in the world is arecanut and is classified as a group 1 carcinogen to human. Over 600 million people are estimated to chew arecanut. It is consumed in some form or other, often with betel quid by 10%–20% of world's population. School goers lack the knowledge and practice toward arecanut use. Many high school children are not well aware about the health hazards of arecanut consumption. Most children start arecanut use at younger age. Arecanut consumption is more prevalent among boys than girls. Arecanut was introduced for majority of the school goers by either their friends or family. Most common form of arecanut used by school goers is sweetened supari. Aim: The current study was conducted to identify the knowledge on health effects, and current practice toward arecanut use among secondary school children living in Malé City, Maldives. Methods: A cross-sectional survey using precoded questionnaire was used. A total of 1350 questionnaire was distributed to the secondary school students studying at selected schools in Malé City and 804 questionnaires were returned. The total target population was 5297. The schools were selected through cluster sampling, while the students were selected via simple random sampling. The calculated sample size consists of 674 students which are equally selected from both genders. Data analyses were executed by using Excel and SPSS 21 software. Descriptive statistics and nonparametric tests were performed. Ethical approval was obtained from Villa College as well from national health research committee at ministry of health. Results: Secondary school children in Malé City have inadequate knowledge on harmful effects of arecanut use. The knowledge varies based on their gender, grade, school, and residence, but does not vary based on their age. The knowledge on harmful effects of arecanut use is more among girls, students at grade 9, students studying at Rehendhi school, and more among those who lives in both Hulhumalé and Vilimalé than those who live in Malé. The students started arecanut use mainly at age between 11-15 years. It was introduced by either friends or family members among large number of school children. Majority of participants used supari as a main form of arecanut, and Rasily supari was the favorite brand of supari among secondary school children. Boys start its use earlier, frequency, duration, and daily consumption is higher among boys than girls. Conclusion: Secondary school children have inadequate knowledge on harmful effects of arecanut use. Supari is the main form of arecanut use and most of the students initiated the habit at a younger age, which is an alarming threat to the society, hence there is an urgent need to start school based preventive programs, community awareness programs, for school children, teachers, parents, and general public at large as stopping the starting of a habit is much easier than quitting of same habit.
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R N, Pushpa. "Review on Detection and Prediction of Diseases in Arecanut Trees." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–3. http://dx.doi.org/10.55041/ijsrem29103.
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In this study, we propose a method for detecting and predicting diseases in arecanut plants using image processing. The proposed method consists of three main steps: image acquisition, image segmentation, and disease detection and prediction. The performance of the proposed method is evaluated using a dataset of arecanut leaf images with various diseases. The results show that the proposed method can accurately detect and predict the presence of diseases in the arecanut plants with high precision and recall rates. Keywords: Arecanut, Machine learning, Convolutional neural networks.
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A. C., Anitha, R. ,. Dhanesha, Shrinivasa Naika C. L., Krishna A. N., Parinith S. Kumar, and Parikshith P. Sharma. "Arecanut Bunch Segmentation Using Deep Learning Techniques." International Journal of Circuits, Systems and Signal Processing 16 (July 26, 2022): 1064–73. http://dx.doi.org/10.46300/9106.2022.16.129.
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Agriculture and farming as a backbone of many developing countries provides food safety and security. Arecanut being a major plantation in India, take part an important role in the life of the farmers. Arecanut growth monitoring and harvesting needs skilled labors and it is very risky since the arecanut trees are very thin and tall. A vision-based system for agriculture and farming gains popularity in the recent years. Segmentation is a fundamental task in any vision-based system. A very few attempts been made for the segmentation of arecanut bunch and are based on hand-crafted features with limited performance. The aim of our research is to propose and develop an efficient and accurate technique for the segmentation of arecanut bunches by eliminating unwanted background information. This paper presents two deep-learning approaches: Mask Region-Based Convolutional Neural Network (Mask R-CNN) and U-Net for the segmentation of arecanut bunches from the tree images without any pre-processing. Experiments were done to estimate and evaluate the performances of both the methods and shows that Mask R-CNN performs better compared to U-Net and methods that apply segmentation on other commodities as there were no bench marks for the arecanut.
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Jamanal, Hanumantappa, and C. Murthy. "A Study on Growth of Procurement of Arecanut by Different Marketing Agencies for the Benefit of the Farmers." Asian Journal of Agricultural Extension, Economics & Sociology 41, no. 8 (2023): 120–26. http://dx.doi.org/10.9734/ajaees/2023/v41i81988.
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The arecanut palm (Areca catechu L.,) is a significant commercial and business crop of India. It plays an important role in the political, social and cultural functions and the economic life of people in our country. From an area of 12.26 lakh hectares of land, the total arecanut production at the global level was 17.96 lakh tonnes in 2019–20.In India, arecanut crop has been cultivated from time immemorial with traditional cultivation techniques and one of the biggest traditional areca-growing countries in the world level. Arecanut is a major and commercial plantation crop cultivated in peninsular and Eastern India. The detailed information needed for the study was gathered from secondary sources. For the study two marketing societies/developmental agencies were selected i.e RAMCOS and TUMCOS. The secondary data was collected from the marketing agencies regarding membership of societies and procurement of arecanut from 2007-08 to 2021-22. In order to arrive at the meaningful results the Descriptive statistics and compound annual growth rate were employed. The compound annual growth rate for number of members of the TUMCOS societies shows positive trend 10.68 per cent. The compound annual growth rate of procurement of arecanut exhibited a growth of 7.14 per cent. The compound annual growth rate for number of members of RAMCOS growth rate was 5.98 per cent. The negative growth rate in procurement of arecanut by the RAMCOS agency (-3.91%). The society is successful in gaining attention of arecanut growers towards the society by providing various schemes like credit facilities to the members, providing agricultural inputs and insurance to the farmers. RAMCOS and TUMCOS is providing fair price for areca nut. The marketing societies are a reputed areca nut marketing society working in Shivamogga and Davanagere district as a role model for other co-operative societies.
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Raghavendra, Narasimha, and Jathi Ishwara Bhat. "Red Arecanut Seed Extract as a Sustainable Corrosion Inhibitor for Aluminum Submerged in Acidic Corrodent: An Experimental Approach Towards Zero Environmental Impact." Periodica Polytechnica Chemical Engineering 62, no. 3 (2017): 351–58. http://dx.doi.org/10.3311/ppch.10686.
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The effect of the red arecanut seed (RAS) extract on the corrosion of aluminum in 0.5 M hydrochloric acid environment is reported by weight loss, electrochemical (Tafel plot and impedance spectroscopy), scanning electron microscopy and atomic force microscopy studies. The weight loss study indicated that, protection efficiency of the red arecanut seed extract is directly proportional to its concentration and inversely proportional to solution temperature and aluminum contact time in the test solution. Langmuir adsorption isotherm is best fitted model explaining the adsorption of red arecanut seed extract constituents on aluminum surface in 0.5 M HCl system. The results obtained from Tafel curves indicated the mixed inhibition role of red arecanut seed extract. The impedance spectroscopy technique indicated that, red arecanut seed extract reduces the speed of aluminum corrosion by charge transfer process. The scanning electron microscopy and atomic force microscopy images of aluminum specimens clearly give clues about the adsorption of plant constituents on the surface of the aluminum metal.
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Raghavendra, Narasimha. "Areca Plant Extracts as a Green Corrosion Inhibitor of Carbon Steel Metal in 3 M Hydrochloric Acid: Gasometric, Colorimetry and Atomic Absorption Spectroscopy Views." Journal of Molecular and Engineering Materials 06, no. 01n02 (2018): 1850004. http://dx.doi.org/10.1142/s2251237318500041.
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The corrosion inhibition of carbon steel (CS) in 3 M HCl was thoroughly investigated in the presence and absence of areca plant extracts as a green corrosion inhibitor. The effect of solution temperature and different amounts of areca plant extracts was examined through gasometric, colorimeter and atomic absorption spectroscopy techniques. Surface studies were screened by scanning electron microscopy (SEM) technique. The results of these techniques show that areca plant extracts (arecanut husk extract, arecanut seed extract and areca flower extract) behave as a nontoxic corrosion inhibitor for CS in 3 M HCl solution. Areca plant extract (arecanut husk extract, arecanut seed extract and areca flower extracts) shows maximum protection efficiency at 4 g/L extract amount.
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S, Kulanthaisami, Subramanian P, Venkatachalam P, and Sampathrajan A. "Drying kinetics of arecanut using solar cum biomass drying system." Madras Agricultural Journal 94, July (2007): 256–68. http://dx.doi.org/10.29321/maj.10.100670.
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Dried arecanut (Areca catechu) is widely used as a component of the betel leaf chewed in India. The arecanut processing industries are currently drying the nuts after boiling of nuts by open sun drying for 8 to 10 days. The moisture content of processed arecanut is reduced from 40 to 11 % during drying operation for safe storage and to maintain food quality. For the drying process, a solar cum biomass based dryer with a capacity of 1 tonne per batch was developed to meet the thermal energy requirement. The dryer consists of PAU (Punjab Agricultural University) packed bed model solar collector of 20m2 area, biomass burner, heat exchanger, air blower and hot air duct. The results showed the system to have a capacity to increase air temperature by 15-20°C above. In addition, the organoleptic evaluation reveals that the arecanut being dried in the solar cum biomass dryer system was completely protected from rain, insects and dust. The dried arecanut was of higher quality in terms of flavor and colour compared to open sun dried product, besides saving of time.
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Et. al., Bharadwaj N. K,. "Classification and Grading of Arecanut Using Texture Based Block-Wise Local Binary Patterns." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 11 (2021): 575–86. http://dx.doi.org/10.17762/turcomat.v12i11.5931.
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Arecanut is a commercial crop typical to high rain fall regions. Arecanut has economic, cultural and medicinal importance, and is categorized into different types depend upon the region which grow and market it consumes.In this paper, an attempt towards grading of Arecanut images is proposed. The proposed approach makes use of global textural feature viz., Local Binary Pattern for feature extraction. Initially, an image is divided into k number of blocks. Subsequently, the texture feature is extracted from each k blocks of the image. The k value is varied and has been fixed empirically. For experimentation purpose, the Arecanut dataset is created using 4 different classes and experimentation is done for whole image and also with different blocks like 2, 4 and 8. Grading of Arecanut is done using Support Vector Machine classifier. Finally, the performance of the grading system is evaluated through metrics like accuracy, precision, recall and F –measure computed from the confusion matrix. The experimental results show that most promising result is obtained for 8 block of the image.
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Thanuja, G., Nagaraja, N. R., Vishnuvardhana, Ravi Bhat, Maruti Prasad, B. N., and Ramesh, S. V. "Investigating the Impact of Zinc on Chlorophyll Content and Leaf Area in Arecanut Seedlings." International Journal of Environment and Climate Change 13, no. 12 (2023): 70–76. http://dx.doi.org/10.9734/ijecc/2023/v13i123662.
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A sand culture experiment was carried out at ICAR-CPCRI, Regional Station, Vittal in the year 2021, to evaluate the impact of different concentrations of Zinc (Zn) on chlorophyll content and leaf area in arecanut seedlings. Eight varieties of arecanut seedlings (Mangala, Swarnamangala, Madhuramangala, Shatamangala, South Kanara local (S K local), Thirthahalli, Sirsi arecanut selection -1 (SAS -1), Hirehalli dwarf) were cultivated in a naturally ventilated glasshouse using sand culture provided with 0.031, 0.093 and 0.156 ppm of Zn. After six months of growth, the seedlings were assessed for chlorophyll a, chlorophyll b, total chlorophyll content, and total leaf area. The results indicated that the chlorophyll content and total leaf area of arecanut seedlings were significantly influenced by different varieties and varying levels of zinc supplementation. Maximum values for both chlorophyll content and total leaf area were observed at a Zn concentration of 0.093 ppm (Z2 level). This study suggests that among the different levels of Zn, a concentration of 0.093 ppm (medium level) is optimal for promoting the growth of arecanut seedlings.
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D. K, Umesha, and Dr J. Venkata Krishna. "Towards Smart Agriculture: A Survey of Deep Learning Applications in Arecanut Image Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28023.
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Deep learning has ignited a revolution in arecanut image analysis, promising transformative accuracy, robustness, and automation in quality assessment. Yet, data scarcity, computational demands, and explainability gaps remain hurdles to achieving its full potential. This review dissects these strengths and limitations, charting a course for future research. We propose tackling data scarcity through domain adaptation and active learning, while unveiling deep learning's decision-making through advanced explainability methods. Recognizing the complexities of arecanut analysis, we advocate for domain- specific architectures and prioritize interdisciplinary collaboration to address ethical considerations, sustainability, and integration with farm management systems. By illuminating these research gaps and charting a path forward, this review empowers deep learning to unlock the true potential of the arecanut industry. Keywords: Arecanut, Deep learning, Convolution Neural Network, U-Net, Smart Agriculture.
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Arekattedoddi Chikkalingaiah, Anitha, RudraNaik Dhanesha, Shrinivasa Naika Chikkathore Palya Laxma, Krishna Alabujanahalli Neelegowda, Anirudh Mangala Puttaswamy, and Pushkar Ayengar. "Segmentation and yield count of an arecanut bunch using deep learning techniques." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 542. http://dx.doi.org/10.11591/ijai.v13.i1.pp542-553.
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Arecanut is one of Southeast Asia’s most significant commercial crops. This work aims at helping arecanut farmers get an estimate of the yield of their orchards. This paper presents deep-learning-based methods for segmenting arecanut bunch from the images and yield estimation. Segmentation is a fundamental task in any vision-based system for crop growth monitoring and is done using U-Net squared model. The yield of the crop is estimated using Yolov4. Experiments were done to measure the performance and compared with benchmark segmentation and yield estimation with other commodities, as there were no benchmarks for the arecanut. U-Net squared model has achieved a training accuracy of 88% and validation accuracy of 85%. Yolo shows excellent performance of 94.7% accuracy for segmented images, which is very good compared to similar crops.
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Jin, Yu, Jiawei Guo, Huichun Ye, Jinling Zhao, Wenjiang Huang, and Bei Cui. "Extraction of Arecanut Planting Distribution Based on the Feature Space Optimization of PlanetScope Imagery." Agriculture 11, no. 4 (2021): 371. http://dx.doi.org/10.3390/agriculture11040371.
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The remote sensing extraction of large areas of arecanut (Areca catechu L.) planting plays an important role in investigating the distribution of arecanut planting area and the subsequent adjustment and optimization of regional planting structures. Satellite imagery has previously been used to investigate and monitor the agricultural and forestry vegetation in Hainan. However, the monitoring accuracy is affected by the cloudy and rainy climate of this region, as well as the high level of land fragmentation. In this paper, we used PlanetScope imagery at a 3 m spatial resolution over the Hainan arecanut planting area to investigate the high-precision extraction of the arecanut planting distribution based on feature space optimization. First, spectral and textural feature variables were selected to form the initial feature space, followed by the implementation of the random forest algorithm to optimize the feature space. Arecanut planting area extraction models based on the support vector machine (SVM), BP neural network (BPNN), and random forest (RF) classification algorithms were then constructed. The overall classification accuracies of the SVM, BPNN, and RF models optimized by the RF features were determined as 74.82%, 83.67%, and 88.30%, with Kappa coefficients of 0.680, 0.795, and 0.853, respectively. The RF model with optimized features exhibited the highest overall classification accuracy and kappa coefficient. The overall accuracy of the SVM, BPNN, and RF models following feature optimization was improved by 3.90%, 7.77%, and 7.45%, respectively, compared with the corresponding unoptimized classification model. The kappa coefficient also improved. The results demonstrate the ability of PlanetScope satellite imagery to extract the planting distribution of arecanut. Furthermore, the RF is proven to effectively optimize the initial feature space, composed of spectral and textural feature variables, further improving the extraction accuracy of the arecanut planting distribution. This work can act as a theoretical and technical reference for the agricultural and forestry industries.
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Sangma, Symon M., and D. C. Kalita. "Analysis of Marketing Pattern of Arecanut Grower in East Garo Hills District, Meghalaya." Environment and Ecology 42, no. 3 (2024): 990–96. http://dx.doi.org/10.60151/envec/cnlh8158.
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Arecanut (Areca catechu L) is one of the important cash crops in India and ranks first in terms of both area and production of arecanut in world. Meghalaya holds 4th position in the production of arecanut in India producing 51,000 tonnes in 2019. The total of 200 samples was selected for the study. The marketing channels were identified based on various intermediaries involved in the marketing process. A total of 4 marketing channels found in the study area of which Channel II (Producer – Village Trader – Whole seller – Consumer) was the most effective channel for marketing arecanut, accounting for nearly 40% of the total marketed quantity. In terms of marketing margin earned by the different marketing channel, channel III was found to be earned the highest marketing margin consists of the marketing margin earned by the wholesaler, village traders and retailer. The Producers share in Consumer’s Rupee (%) was found to be highest in Channel I. The study of arecanut marketing channels offers valuable lessons for the broader agricultural sector, suggesting pathways to enhance producer income, reduce marketing costs, and improve the overall efficiency of agricultural marketing systems.
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Chikkalingaiah, Anitha Arekattedoddi, RudraNaik Dhanesha, Shrinivasa Naika Chikkathore Palya Laxmana, Krishna Alabujanahalli Neelegowda, Anirudh Mangala Puttaswamy, and Pushkar Ayengar. "Segmentation and yield count of an arecanut bunch using deep learning techniques." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 542–53. https://doi.org/10.11591/ijai.v13.i1.pp542-553.
Full textAbstract:
Arecanut is one of Southeast Asia’s most significant commercial crops. This work aims at helping arecanut farmers get an estimate of the yield of their orchards. This paper presents deep-learning-based methods for segmenting arecanut bunch from the images and yield estimation. Segmentation is a fundamental task in any vision-based system for crop growth monitoring and is done using U-Net squared model. The yield of the crop is estimated using Yolov4. Experiments were done to measure the performance and compared with benchmark segmentation and yield estimation with other commodities, as there were no benchmarks for the arecanut. U-Net squared model has achieved a training accuracy of 88% and validation accuracy of 85%. Yolo shows excellent performance of 94.7% accuracy for segmented images, which is very good compared to similar crops.
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Kailashkumar.B, Anandan D, Gopikrishnan R, Naveena Nava Bharathi P T, Muthupriya M, and Jeya Shanthi M. "Design and Development of a Remote Controlled Arecanut Harvesting System." Journal of Scientific Research and Reports 31, no. 6 (2025): 307–16. https://doi.org/10.9734/jsrr/2025/v31i63130.
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Arecanut harvesting is a labor-intensive and hazardous process, particularly in regions with dense plantations. The conventional method of manual plucking is time-consuming, physically demanding, and often results in damage to both the nuts and the trees. This project aims to design and develop a remote-operated arecanut harvester to improve harvesting efficiency, reduce labor costs, and enhance worker safety. The proposed harvester features an arm with a specialized cutting blade for gentle nut removal and a remote-control system for operator convenience. The harvester's performance was evaluated based on harvesting efficiency, nut damage, and operator safety. The remote-operated arecanut harvester demonstrates the potential to transform the harvesting process, offering significant benefits to farmers, workers, and the industry as a whole. This innovation can contribute to increased productivity, improved product quality, and reduced occupational hazards in the arecanut sector.
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VASTRAD, JYOTI V., GIRIDHAR GOUDAR, SHAMEEMBANU A. BYADGI, and DEEPA S. BHAIRAPPANNAVAR. "Areca catechu Slurry: A Rich Source of Phenolics and Flavonoids." Asian Journal of Chemistry 33, no. 2 (2021): 271–75. http://dx.doi.org/10.14233/ajchem.2021.22913.
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In present study, the qualitative and quantitative analysis of phenolic compounds and flavonoids in arecanut slurry based on UV spectrometry and LC-MS were carried out. Results revealed that the arecanut slurry powder extract exhibited the presence of different phenolic groups such as alkaloids, flavonoids, phenolic acids, tannins, saponins and terpenoids. Further, total phenolic content (TPC) and total flavonoid content (TFC) of aqueous extract of areca slurry powder was found to be 214.50 mg/g (GAE) and 184.12 mg/g (RE), respectively. LC-MS analysis depicted the presence of vanillic acid in considerable amounts, which is a benzoic acid derivative used as a flavouring agent. Meanwhile, catechin was profoundly present in the aqueous extracts of arecanut slurry powder among all the other flavonoids. The arecanut slurry powder extracts exhibited substantial amount of vanillic acid and catechin, which are known to be beneficial in various pharmacological studies.
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Mulla, Asif Iqbal, Ayaz Sab, and Abdullah Gubbi. "Arecanut Segregation System Using Local Binary Pattern and HOG Features." International Journal of Engineering Research in Electrical and Electronics Engineering 9, no. 1 (2022): 8–13. http://dx.doi.org/10.36647/ijereee/09.01.a002.
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Agriculture is, doubtlessly, one of the most relevant fields that drive Indian economy. India produces diverse types of spices and seeds depending upon the different soil and climate conditions. India is also one among the cultivator of arecanut and much of its production happens in the coastal region. It is a tropical crop. There is a variation in the quality of arecanut that makes it classified into various types. The arecanut classification and segregation is necessary, basically segregation is done manually which consumes much time, more effort and more error prone. This paper proposes an automatized approach of classification and segregation using hardware and digital image processing
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B, Rajesh, and Ananda KS. "Study of genetic diversity and identification of promising acceassions of arecanut (Areca Catechu L.)." Forestry Research and Engineering: International Journal 3, no. 2 (2019): 39–44. http://dx.doi.org/10.15406/freij.2019.03.00076.
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The study was undertaken to assess and analysis of genetic diversity and to identify the donor parents with desirable traits among the selected 50 arecanut accessions including exotic and indigenous accessions by principal component analysis (PCA) method. The PCA of vegetative, reproductive, nut and yield characters indicated that the first 13 principal components accounted for 81.41 per cent of the variation among the accessions studied. The characters stem height, length of leaf, number of leaflets on right and left side, number of nodes, number of midribs on right side, male phase (days), female phase (days), intra-spadix overlapping (days), number of tertiary rachillae, fresh fruit weight, husk thickness in fresh fruit, dry kernel weight, dry fruit weight and kernel length were found to be contribute to genetic divergence among the arecanut accessions based on PCA. Based on their performance and genetic divergence, the accessions VTL-12 (Saigon), VTL-18III (BSI), VTL-29IV (Andaman), VTL-29II (Andaman), VTL-85 (Ratnagiri), VTL-73 (Kahikuchi), VTL-78 (Saragoan) and VTL-97 (Wynad) could be useful in arecanut breeding programmes. Accession VTL-56, a dwarf arecanut accession has been exploited in the breeding dwarf hybrids.
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Naik, Prof Mahadev. "Design And Development Of Arecanut Harvesting System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50917.
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- Arecanut harvesting is a labor-intensive process that poses significant challenges in terms of efficiency and productivity. This paper presents an innovative arecanut harvesting machine designed to streamline the harvesting process, reduce labor costs, and enhance yield quality. The machine incorporates advanced technology, Field tests demonstrate a marked increase in harvesting speed and a reduction in damage to the nuts compared to traditional methods. The machine features a mechanical pulley system integrated with a precision-engineered cutting mechanism mounted on a lightweight, telescopic pole. The harvesting operation is fully ground-based, significantly eliminating the risks associated with tree climbing. In field trials, the machine demonstrated an average reduction in harvesting time by 40–50% compared to manual methods, while nut damage was reduced to less than 3%. In addition, the machine requires minimal operator training, is portable, low-maintenance, and affordable, making it particularly suitable for small and medium-scale farmers. This study concludes that the adoption of mechanization in arecanut harvesting is essential for meeting the growing global demand and improving the livelihoods of farmers in arecanut-producing regions. Key Words: optics, photonics, light, lasers, templates, journals
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Kumar, K. Arun, Shanmukha N. T, Lokeshappa B, and Vinayaka M. "Kinetic Studies On Reactive Dye Removal From Aqueous Solution By Using Arecanut Peel." International Journal of Membrane Science and Technology 9, no. 2 (2022): 155–64. http://dx.doi.org/10.15379/ijmst.v9i2.3679.
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This research aims at finding the effectiveness of Remazole Red RGB dye removal using arecanut peel, an agricultural waste, as an activated carbon. The arecanut peel-activated carbon was prepared in the laboratory by carbonization followed by activation. Adsorption studies were carried out to look for the effect of different experimental scenarios, like different pH values, varying contact times, the initial concentration of dye, and changing arecanut peel carbon dosage, on the removal efficiency of Remazole Red RGB dye from the experimental solution. The equilibrium experimental results were checked for the applicability of the Langmuir and Freundlich isotherm models and the kinetic models. The batch test result was a maximum dye removal of 83% with an initial dye concentration of 5 mg/L at an adsorbent dose of 0.625 g/L at dye pH 4 in a 50-minute time span. For Remazole Red RGB dye removal, the test result is unfavorable for the Langmuir isotherm model but suits well for he Freundlich i isotherm model. The maximum adsorption capacity of arecanut peel carbon on Remazole Red RGB dye was 3.89 mg/g. It was evident that the adsorption process is favorable for the pseudo-second-order rate kinetics. It was seen that intra-particle diffusion is not the only rate-limiting step in this adsorption experimental system; also, regression results show that the linear regression model gives the best outcome. The end result of this study confirms that powder arecanut peel activated carbon was the right option for removing reactive dye from an aqueous solution.
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Lei, Shuhan, Jianbiao Luo, Xiaojun Tao, and Zixuan Qiu. "Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors." Remote Sensing 13, no. 22 (2021): 4562. http://dx.doi.org/10.3390/rs13224562.
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Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). However, the correlation between the severity of the yellow leaf disease of arecanut and LVV must be demonstrated through research. Therefore, this study aims to apply the multispectral data obtained by the UAV along with the high-resolution UAV remote sensing images to obtain five vegetation indexes such as the normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), leaf chlorophyll index (LCI), green normalized difference vegetation index (GNDVI), and normalized difference red edge (NDRE) index, and establish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut, which is expressed by the proportion of the yellowing area of a single areca crown (in percentage). The traditional qualitative expression of this disease is transformed into the quantitative expression of the yellow leaf disease of arecanut per plant. The results demonstrate that the classification accuracy of the test set of the BPNN algorithm and SVM algorithm is the highest, at 86.57% and 86.30%, respectively. Additionally, the UAV structure from motion technology is used to measure the LVV of a single areca tree and establish a model of the correlation between the LVV and the severity of the yellow leaf disease of arecanut. The results show that the relative root mean square error is between 34.763% and 39.324%. This study presents the novel quantitative expression of the severity of the yellow leaf disease of arecanut, along with the correlation between the LVV of areca and the severity of the yellow leaf disease of arecanut. Significant development is expected in the degree of integration of multispectral software and hardware, observation accuracy, and ease of use of UAVs owing to the rapid progress of spectral sensing technology and the image processing and analysis algorithms.
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Pragasan, L. Arul, and Sidharth Madhu. "Assessment of Carbon Stock Potential of Arecanut Plantations in Coimbatore District of Tamil Nadu, India." Asian Journal of Research in Agriculture and Forestry 11, no. 3 (2025): 148–58. https://doi.org/10.9734/ajraf/2025/v11i3420.
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Climate change, driven largely by anthropogenic greenhouse gas emissions, demands the identification of sustainable carbon sequestration strategies. Agroforestry systems, particularly plantation crops like arecanut (Areca catechu L.), have gained attention for their potential to serve as carbon sinks while offering socio-economic benefits. Despite widespread arecanut cultivation in India, its role in climate mitigation remains underexplored. This study aims to address the existing knowledge gap by quantifying the biomass and carbon stock of arecanut plantations in the Coimbatore district of Tamil Nadu, India. This study quantifies the biomass and carbon stock potential of arecanut plantations at two managed sites, Onappalayam (Site 1) and Vedapatti (Site 2) in the Coimbatore district of Tamil Nadu, India. Each site comprises a 1-hectare plantation with different intercrops (teak at Site 1 and coconut at Site 2) and crop spacing. Using a standardized quadrat method (25 quadrats per hectare), tree girth measurements were collected to estimate above-ground and below-ground biomass through established allometric equations. To estimate biomass, both above-ground biomass (AGB) and below-ground biomass (BGB) were calculated using standard allometric equations. Total carbon stock was calculated as 50% of the total biomass. Results revealed significantly higher tree density, biomass, and carbon stock at Site 1 compared to Site 2. Site 1 recorded a mean total biomass of 3.04 ± 0.95 tonnes/quadrat and total carbon stock of 1.52 ± 0.48 tonnes/quadrat, while Site 2 reported 1.61 ± 0.52 tonnes/quadrat and 0.80 ± 0.26 tonnes/quadrat, respectively. Differences were attributed to higher tree density and better soil potassium levels at Site 1. Above-ground carbon stock accounted 85% of total carbon, underscoring the dominant role of canopy biomass in carbon sequestration. This study demonstrates that arecanut plantations, beyond their economic value, possess substantial carbon storage potential. Given their long lifespan and adaptability, arecanut systems can contribute meaningfully to climate change mitigation efforts. The findings advocate for their inclusion in agroforestry-based carbon accounting and climate policy frameworks, particularly in tropical regions where such systems are already well established. As global concerns about climate change grow, it becomes increasingly important to recognize the role of agricultural and plantation systems in climate mitigation strategies. This study underscores the contribution of arecanut plantations to carbon sequestration and advocates for their integration into broader agroforestry systems. When managed sustainably, these plantations offer multiple benefits: they not only absorb atmospheric carbon but also improve soil quality, and provide steady income to farmers.
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Jayaswal, Abhishek, Saurabh Goel, Kavita Verma, Srujal Jivrajani, and Barkha Makhijani. "Prevalence of oral submucous fibrosis linking with Areca Nut usage among Indians." Bioinformation 20, no. 7 (2024): 751–53. http://dx.doi.org/10.6026/973206300200751.
Full textAbstract:
The prevalence of oral submucous fibrosis amongst population of Southern Rajasthan is of interest to dentists. Hence, a cross sectional study had been conducted on 3548 patients from 18-60 years age group who visited the Department of the Oral Medicine and Radiology, Pacific Dental College and Research Centre, Bedla, Udaipur was completed. They were subjected to thorough case history related to quid habit in arecanut (Areca catechu L.) form and to diagnose OSMF clinically. 1645 processed form of arecanut users were identified. The prevalence of arecanut chewers in study population has been reported to be 46.36%. The prevalence of OSMF in study population has been reported to be 458 (12.9%). Exact component associated and the mechanism involved in the occurrence of OSMF is still not available in literature and research is going on regarding unveiling this mysterious lesion.
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Chowdappa, P., K. B. Hebbar, and S. V. Ramesh. "Arecanut and Human Health." Current Science 115, no. 6 (2018): 1025. http://dx.doi.org/10.18520/cs/v115/i6/1025-1026.
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D, SUBBARAJ, and RAMASWAMI P.P. "ORGANIC AMENDMENTS ON PROTEIN AND OIL YIELD OF GROUNDNUT UNDER Theri SOILS (TYPIC USTIPSAMMENTS)." Madras Agricultural Journal 82, February (1995): 119–21. http://dx.doi.org/10.29321/maj.10.a01142.
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A pot experiment was conducted with the object of studying the different locally available organic amendments on yield, protein and oil content of groundnut under theri soils. Arecanut waste recorded the highest protein content (17.81 per cent). Biogas slurry applied treatment recorded the highest oil content, (48.4 per cent) The treatment with the application of arecanut waste gave the highest pod yield(19.00 g/pot) and total oil yield (4.9 g/pot)
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Vinayagam, S. Senthil, K. Akhila, Pooja S. Bhat, et al. "Interdisciplinary approaches for translating rural innovations into agripreneurship." AGRICULTURE UPDATE 15, no. 4 (2020): 301–7. http://dx.doi.org/10.15740/has/au/15.4/301-307.
Full textAbstract:
Agripreneurship converts agricultural activity into an entrepreneurial activity. In agriculture and allied sectors the agripreneur adopting innovative ideas. The purpose of this study is to promote agripreneurship in plantation sector through interdisciplinary approach. Plantation crops are high value crops and any breakthrough in translation of relevant rural innovation into agripreneurship can have a greater economic impact. The experts from different domains interacted with the rural innovators about Farmer Led Innovations (FLIs). They were found more than hundred FLIs in plantation sector out of which few FLIs have been identified based on Innovation Index for translating FLIs into agripreneurship. The selected FLIs are viz., tree climber (Device to climb coconut and arecanut tree), multi tree climber, arecanut tree climber, tree walker and automated arecanut climbing and harvesting machine. The outcome of the study has given leverage technological strength to showcase successful FLIs and successful agri-business models across the country.
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Vinayagam, S. Senthil, K. Akhila, Pooja S. Bhat, et al. "Interdisciplinary approaches for translating rural innovations into agripreneurship." AGRICULTURE UPDATE 15, no. 4 (2020): 301–7. http://dx.doi.org/10.15740/has/au/15.4/301-307.
Full textAbstract:
Agripreneurship converts agricultural activity into an entrepreneurial activity. In agriculture and allied sectors the agripreneur adopting innovative ideas. The purpose of this study is to promote agripreneurship in plantation sector through interdisciplinary approach. Plantation crops are high value crops and any breakthrough in translation of relevant rural innovation into agripreneurship can have a greater economic impact. The experts from different domains interacted with the rural innovators about Farmer Led Innovations (FLIs). They were found more than hundred FLIs in plantation sector out of which few FLIs have been identified based on Innovation Index for translating FLIs into agripreneurship. The selected FLIs are viz., tree climber (Device to climb coconut and arecanut tree), multi tree climber, arecanut tree climber, tree walker and automated arecanut climbing and harvesting machine. The outcome of the study has given leverage technological strength to showcase successful FLIs and successful agri-business models across the country.
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Hongal, Shivanand, T. V. Sowjanya, Sudheesh Kulkarni, et al. "Different Farming Systems Concerning Soil Health and Yield of Arecanut and Black Pepper." International Journal of Plant & Soil Science 35, no. 18 (2023): 1722–30. http://dx.doi.org/10.9734/ijpss/2023/v35i183452.
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We conducted two year (2020 and 2021) field experiment in the farmer’s field at Hanagal, Sirsi, Karnataka to study the impacts of different farming practices (Recommended package of practice; (RPP), Organic farming, Natural farming and Chemical farming) on rhizosphere microflora, soil nutrient status and yield of arecanut and black pepper. The results revealed that, soil pH and electrical conductivity did not vary significantly due to different farming systems. Whereas, the significantly (p<0.05) highest soil organic carbon content was in organic farming (0.74%) which was on par with natural farming (0.66%) and least in chemical farming (0.71%). The highest available nitrogen (258.31 kg ha-1), phosphorus (39.06 kg ha-1) and potassium (205.47 kg ha-1) were in RPP. Whereas the highest secondary nutrients and micronutrients content were in organic and natural faming. The lowest of all these nutrients were recorded in chemical farming at the harvest stage of arecanut. Soil microflora, dehydrogenase and phosphatase activity in the arecanut and black pepper rhizosphere were significantly (p<0.05) highest in natural farming and lowest in chemical farming. Concerning yield, the significantly highest arecanut (Chali yield 29.35 q.ha-1) and black pepper (dry yield 12.07 q. ha-1) yield was in RPP and maximum net return also observed in RPP (Rs. 10, 62, 500 ha-1).