Relevant bibliographies by topics / Crop categorization

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Crop categorization'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Crop categorization"

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Pompapathi, Manasani, Shaik Khaleelahmed, Malik Jawarneh, et al. "Effective crop categorization using wavelet transform based optimized long short-term memory technique." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2309–18. https://doi.org/10.11591/eei.v14i3.7748.

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Effective crop categorization is important for keeping track of how crops grow and how much they produce in the future. Gathering crop data on categories, regions, and space distribution in a timely and accurate way could give a scientifically sound reason for changes to the way crops are organized. Polarimetric synthetic aperture radar dataset provides sufficient information for accurate crop categorization. It is essential to classify crops in order to successfully. This article presents wavelet transform (WT) based optimizedlong short-term memory (LSTM) deep learning (DL) for effective crop
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Amanullah Ansari, Shrejal Singh, and Dr. Nikhat Akhtar. "AI-Driven Crop Disease Detection and Management in Smart Agriculture." International Journal of Scientific Research in Science and Technology 12, no. 3 (2025): 309–19. https://doi.org/10.32628/ijsrst2512341.

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Agriculture is a fundamental component of human civilization. It contributes to the economy while also providing sustenance. Plant foliage or crops are susceptible to many illnesses during agricultural agriculture. The illnesses impede the development of their respective species. Timely and accurate identification and categorization of illnesses may mitigate the risk of further harm to the plants. The identification and categorization of these disorders have emerged as significant challenges. The conventional methods used by farmers to anticipate and categorize plant leaf diseases may be tedio
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K.H, Sandeep. "Crop and Pest Classification Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43290.

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Crop pests pose a hazard to agriculture by lowering yields and creating large losses. Timely intervention depends on prompt and precise pest identification. Convolutional Neural Networks (CNNs), a type of deep learning, are used in this study to effectively classify pests. To improve performance, the method places a strong emphasis on image preprocessing, accurate pest segmentation, and transfer learning. The algorithm is trained on a large dataset of photos of pests and non-pests to find distinctive characteristics for precise categorization. With an emphasis on improved image quality, segmen
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Ayoola, Adefemi Joshua, Joe Essien, Martin Ogharandukun, and Felix Uloko. "Data-Driven Framework for Crop Categorization using Random Forest-Based Approach for Precision Farming Optimization." European Journal of Computer Science and Information Technology 12, no. 3 (2024): 15–25. http://dx.doi.org/10.37745/ejcsit.2013/vol12n31525.

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Making incorrect choices when selecting crops can result in substantial financial losses for farmers, primarily because of a limited understanding of the unique needs of each crop. Each farm possesses unique characteristics, influencing the effectiveness of modern agricultural solutions. Challenges persist in optimizing farming methods to maximize yield. This study aims to mitigate these issues by developing a data-driven crop classification and cultivation advisory system, leveraging machine learning algorithms and agricultural data. By analysing variables such as soil nutrient levels, temper
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Micaelo, Eduardo B., Leonardo G. P. S. Lourenço, Pedro D. Gaspar, João M. L. P. Caldeira, and Vasco N. G. J. Soares. "Bird Deterrent Solutions for Crop Protection: Approaches, Challenges, and Opportunities." Agriculture 13, no. 4 (2023): 774. http://dx.doi.org/10.3390/agriculture13040774.

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Weeds, pathogens, and animal pests are among the pests that pose a threat to the productivity of crops meant for human consumption. Bird-caused crop losses pose a serious and costly challenge for farmers. This work presents a survey on bird deterrent solutions for crop protection. It first introduces the related concepts. Then, it provides an extensive review and categorization of existing methods, techniques, and related studies. Further, their strengths and limitations are discussed. Based on this review, current gaps are identified, and strategies for future research are proposed.
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Silva, Jackelya, Marcelo Angelo Cirilo, Flávio Meira Borém, Diego Egídio Ribeiro, and Loureço Manuel. "Sensorial analysis of categorized data of special coffee to identify similar crop seasons pairs using Kappa." Brazilian Journal of Biometrics 41, no. 1 (2023): 30–43. http://dx.doi.org/10.28951/bjb.v41i1.590.

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This paper presents the proposal of a statistical method to analyse dependent agreement data with categorical ordinal responses for a longitudinal study in sensorial analysis of special coffee. The assessment of sensory attributes of special coffees were carried out by certified raters using a continuous scale of grades. The approach aimed to applying data categorization methods commonly used in machine learning which generated not only a concise summary of continuous attributes to describe the data but also allowed to maximize the agreement grades in a longitudinal study. A previous analysis
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Shahid, Mohammad, D. K. Bhandari,, A. P. Singh, and Intjar Ahmad. "Groundwater Quality Appraisal and Categorization in Pillu Khera Block of Jind District, Haryana (India)." Asian Journal of Water, Environment and Pollution 6, no. 4 (2009): 67–71. http://dx.doi.org/10.3233/ajw-2009-6_4_09.

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A study has been carried out for the quality appraisal of the groundwater of Pillu Khera block of Jind district in Haryana state, India. For the study 150 tube-well water samples from 23 villages of Pillu Khera block were collected during March 2004. Dominant cation in irrigation water was sodium followed by calcium and magnesium. Likewise, in case of anions, chloride was the dominant ion followed by bicarbonate and sulphate. RSC was observed only in tube-well waters having EC upto 5 dS m $^{-1}$ and subsequent EC range of water did not show presence of RSC. Maximum number of underground water
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Pore, Prof Yogita, Suraj Teli, Swaraj Ghuge, and Nikhil Patil. "Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1767–70. http://dx.doi.org/10.22214/ijraset.2023.51405.

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Abstract: Early disease identification is crucial for productive crop production in agriculture. illnesses such as bacterial spot, late blight, Septoria leaf spot, and yellow curved leaf the quality of the tomato harvest. Automatic classification techniques of plant diseases also assist in taking action once they are discovered diseased leaf symptoms Presented below is a Convolutional Learning Vector Quantization and Neural Network (CNN) model Method for detecting tomato leaf disease based on the (LVQ) algorithm and categorization. There are 500 tomato photos in the dataset. leaves that displa
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Yadav, Rakesh Kumar, Manoj Kumar Tripathi, Sushma Tiwari, et al. "DUS-Based Morphological Profiling and Categorization of Chickpea (Cicer arietinum L.) Genotypes." Current Journal of Applied Science and Technology 42, no. 40 (2023): 20–36. http://dx.doi.org/10.9734/cjast/2023/v42i404259.

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In the realm of plant breeding, genetic diversity stands as a pivotal factor for advancing crop improvement initiatives. Morphological characterization assists as a critical role, allowing for the scrutiny of discernible traits in crop plants as this facilitates the identification, classification, and comprehension of genetic variations present among diverse genotypes. The objective of this investigation was to scrutinize the morphological traits of 71 chickpea genotypes, with a particular emphasis on 10 selected qualitative traits, in adherence to the DUS testing guidelines. The experimental
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Olaniyi, Olumuyiwa. "Categorization of Rural Youth on Utilization of Agricultural Information on Arable Crop in Southwest Nigeria." American Journal of Experimental Agriculture 3, no. 3 (2013): 571–78. http://dx.doi.org/10.9734/ajea/2013/2629.

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Dissertations / Theses on the topic "Crop categorization"

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Arias, Eduardo Fernando. "CATEGORIZATION OF SOIL SUITABILITY TO CROP SWITCHGRASS AT MISSISSIPPI, US USING GEOGRAPHIC INFORMATION SYSTEM, MULTICRITERIA ANALYSIS AND SENSITIVITY ANALYSIS." MSSTATE, 2008. http://sun.library.msstate.edu/ETD-db/theses/available/etd-04042008-095516/.

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Switchgrass (Panicum virgatum) has been widely investigated because of its notable properties as an alternative pasture grass and as an important biofuel source. The goal of this study was to determine soil suitability for Switchgrass in Mississippi. A linear weighted additive model was developed to predict site suitability. Multicriteria analysis and Sensitivity analysis were utilized to optimize the model. The model was fit using seven years of field data associated with soils characteristics collected from NRCS-USDA. The best model was selected by correlating estimated biomass yield with ea
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Book chapters on the topic "Crop categorization"

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Padshetty, Smitha, and Ambika. "Enhancing Crop Infection Categorization: Introducing a Novel MobilenetV1 based Oppositional Crayfish Algorithm." In Advances in Intelligent Systems Research. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-738-0_63.

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Gaikar, Vilas, Caroleena Rane, Narendra Mustare, and Rajiv Nayan. "Implementation of DCNN Framework—Auto Identification and Categorization of Various Stress in Paddy Crop and Resource Management System." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9839-1_18.

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Piccioli, Marianna. "Lo stigma e le sue intersezioni. Effetti moltiplicatori di abilismo e genere." In Politiche e strategie per l’uguaglianza di genere e l’inclusione. Temi, ricerche e prospettive dei CUG delle Università di Siena e Firenze. Firenze University Press, USiena Press, 2025. https://doi.org/10.36253/979-12-215-0715-7.09.

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With this contribution, we tried to analyze the amplifying effects of stigma, in particular when ableism is combined with gender, highlighting how this perspective can also be reductionist. In particular, the processes of stigmatization of multiple identities are examined, emphasizing that stigma manifests itself on individuals who contain characteristics considered undesirable, in contrast with an ideal of "perfect human being" which, in reality, is a social, political and economic construction. It explores how the mechanisms of categorization, labeling and discrimination ar
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"Chapter 2 Essentials of crop nutrition and plant nutrient categorization." In Crop Nutrition. De Gruyter, 2024. http://dx.doi.org/10.1515/9783111617671-002.

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Kapse, Manohar, Vinod Sharma, Jeanne Poulose, N. Elangovan, and Yogesh Mahajan. "Cotton Health-Guard." In Industrial Applications of Big Data, AI, and Blockchain. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1046-5.ch009.

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Crop loss due to illness is the main issue that farmers deal with. The second issue is the delay in identifying which disorders to treat. Thus, the purpose of this study is to use the image classification technique to determine if the crop is healthy or sick. R software was used to implement picture categorization and machine learning techniques. The diseased leaf has been identified by the process of picture classification. In order to accomplish this, images of both healthy and diseased cotton crops were gathered from the fields. According to the study, the support vector machine algorithm i
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Chikkamath, Manjunath, Dwijendra Nath Dwivedi, Rajashekharappa Thimmappa, and Kyathanahalli Basavanthappa Vedamurthy. "Detection and Categorization of Diseases in Pearl Millet Leaves using Novel Convolutional Neural Network Model." In Future Farming: Advancing Agriculture with Artificial Intelligence. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815124729123010006.

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Pearl millet is a staple food crop in areas with drought, low soil fertility, and higher temperatures. Fifty percent is the share of pearl millet in global millet production. Numerous types of diseases like Blast, Rust, Bacterial blight, etc., are targeting the leaves of the pearl millet crop at an alarming rate, resulting in reduced yield and poor production quality. Every disease could have distinctive remedies, so, wrong detection can result in incorrect corrective actions. Automatic detection of crop fitness with the use of images enables taking well-timed action to improve yield and in th
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Kindra, Khushal, and Bhuvaneswari Amma N. G. "Crop Prediction for Smart Agriculture Using Ensemble of Classifiers." In Advances in Environmental Engineering and Green Technologies. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-9975-7.ch007.

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Nowadays due to the advancement in technology, smart agriculture is in the evolving stage. Agricultural farmers worldwide commonly utilize the process of cultivating and harvesting crops to produce food and fiber. Therefore, crop prediction is vital for smart agriculture and the proposed approach involves utilizing all the necessary resources to facilitate crop growth and maintenance. Crop cultivation used to be carried out based on farmers' actual experience. For farmers and agricultural decision-makers to make prompt and accurate judgments that will impact the caliber of agricultural harvest
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Choudhury, Rajashri Roy, Piyal Roy, and Shivnath Ghosh. "Plant Disease Classification in Segmented Images Using Computer Vision." In Advances in Environmental Engineering and Green Technologies. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-9975-7.ch004.

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Agriculture productivity has a significant impact on the lives of people and economies because of the growing human population. In agriculture, plant diseases are a big problem since they result in severe crop losses and financial hardship for farmers. Traditional disease detection and categorization methods take a long time and are subjective, so automated and effective methods are required. Computer vision techniques have recently shown promise as tools for classifying plant diseases. To provide a precise and dependable system for disease detection and management, this article gives a thorou
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Lee, Julia H. "The Jim Crow Train in African American Literature." In The Racial Railroad. NYU Press, 2022. http://dx.doi.org/10.18574/nyu/9781479812752.003.0006.

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This chapter examines literary representations of African American passengers riding Jim Crow and how the spatialization of the train car contributes to constructions of Black identity. Most of these texts explored in this chapter, including Ralph Ellison’s “Boy on a Train” (written ca. 1937, published 1996), James Weldon Johnson’s novel The Autobiography of an Ex-Colored Man (1912), W. E. B. Du Bois’s essay “The Superior Race” (1923), and Anna Julia Cooper’s A Voice from the South (1892), are set in the era of Jim Crow, but the chapter also explores narratives of Jim Crow in slave narratives
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Bray, Francesca, Barbara Hahn, John Bosco Lourdusamy, and Tiago Saraiva. "Reproductions." In Moving Crops and the Scales of History. Yale University Press, 2023. http://dx.doi.org/10.12987/yale/9780300257250.003.0007.

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Reproductions reflects upon what many scholars consider the beginning of the story—a plant’s reproduction and how it shapes the cropscape associated with that plant. Its history of technology perspective emphasizes the ways production inheres in all processes of reproduction, highlighting the different technologies involved in reproducing a cropscape in place and extending its reach in space and time. It considers three successive phases of crop reproduction. “Breeding” takes germplasm as the cropscape component that best reveals the socio-material components of the cropscape. “Blendings” shif
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Conference papers on the topic "Crop categorization"

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Sharma, Devanshi, Diksha Sharma, Arya Tiwari, Asheesh Tiwari, and Ajeet Kumar Sharma. "Crop Disease Categorizations Using Optimized Machine Learning." In 2024 International Conference on Communication, Control, and Intelligent Systems (CCIS). IEEE, 2024. https://doi.org/10.1109/ccis63231.2024.10931987.

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K, Arjun, Linu Shine, and Deepak S. "Pest Detection and Disease Categorization in Tomato Crops using YOLOv8." In 2024 IEEE Recent Advances in Intelligent Computational Systems (RAICS). IEEE, 2024. http://dx.doi.org/10.1109/raics61201.2024.10690151.

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Prakash, M. Gnana, and D. Sungeetha. "Categorization and Recognition of Pest in Crops and Remedial action for Smart Agriculture." In 2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2024. https://doi.org/10.1109/iceca63461.2024.10801033.

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Murugan, M. Senthil, D. Sungeetha, K. Vijaya, A. Gnana Soundari, R. Dhanalakshmi, and S. Gomathi. "Detection and Categorization of Sorghum Crop using MCRNN Architecture." In 2023 4th International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2023. http://dx.doi.org/10.1109/icosec58147.2023.10275812.

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Domingues, Pedro Henrique Silva, Renan Martins Mendes da Silva, Ibrahim Jamil Orra, Matheus Elias Cruz, Tatiany Marcondes Heiderich, and Carlos Eduardo Thomaz. "Neonatal Face Mosaic: An areas-of-interest segmentation method based on 2D face images." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/wvc.2021.18914.

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The daily life of preterm babies may be involved with long exposure to pain, causing problems in the development of the nervous system. In this context, an on-going area of research is the scientific development of image-based automatic pain detection systems based on several techniques, from anatomical measurements to artificial intelligence, they have generally two main issues: the categorization of the most relevant facial regions for identifying neonatal pain and the practical difficulty related to the presence of artifacts obstructing parts of the face. This paper proposes and implements
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Landewé, R., J. Sieper, U. Kiltz, X. Wang, M. Li, and J. Anderson. "OP0248 Potential differences in axial spondyloarthritis disease activity categorization using different minimum values for high-sensitivity crp in ankylosing spondylitis disease activity score calculation and different definitions of disease flare." In Annual European Congress of Rheumatology, EULAR 2018, Amsterdam, 13–16 June 2018. BMJ Publishing Group Ltd and European League Against Rheumatism, 2018. http://dx.doi.org/10.1136/annrheumdis-2018-eular.2636.

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