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Journal articles on the topic 'Crop categorization'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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11

Svobodová, J., and H. Urbancová. "Statistical Results of Activities Categorization in Czech Agricultural Companies." Scientia Agriculturae Bohemica 47, no. 3 (2016): 135–45. http://dx.doi.org/10.1515/sab-2016-0020.

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Abstract In today’s competitive environment, to identify and correctly adjust the individual components of the business model is an important strategic device for every entrepreneur. This paper (preliminary study) deals with different types of business models applied to the sector of small and medium-size farms in the Czech Republic. The main objective was to identify and categorize activities undertaken by Czech farmers into homogeneous clusters and offer recommendations on possible business model modification. The research was based on data from the Farm Accountancy Data Network (hereafter F

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Preethi, S., Kumaraperumal Ramalingam, Sellaperumal Pazhanivelan, Dhanaraju Muthumanickam, Ragunath Kaliaperumal, and Nivas Raj Moorthi. "Comparing the Effectiveness of Different Machine Learning Algorithms for Crop Cover Classification Using Sentinel 2." International Journal of Environment and Climate Change 13, no. 10 (2023): 571–82. http://dx.doi.org/10.9734/ijecc/2023/v13i102688.

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Crop cover mapping is an essential tool for controlling and enhancing agricultural productivity. By determining the spatial distribution of different crop types, solidified judgements regarding crop planning, crop management, and risk management can be made. Crop cover classification using optical data pose constraints in terms of spatial and spectral resolution. With Sentinel – 2 data providing the ground information at 10m resolution, users may choose the best spectral band combinations and temporal frame by analysing the spectral-temporal information of different crops. The crop categorizat

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Balasani Raghupathi and Dr. Amit Sharma. "Optimizing Chilli Crop Disease Classification Models through Hybrid Differential Evolution and Simulated Annealing." International Journal of Scientific Research in Science and Technology 11, no. 6 (2024): 768–79. https://doi.org/10.32628/ijsrst241161132.

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Crop diseases caused by chillies are a serious threat to food security and agricultural production. Timely intervention and mitigation efforts are contingent upon an accurate categorization of these disorders. Here, we provide a new method that combines the methods of Simulated Annealing (SA) and Differential Evolution (DE) to improve classification models for illnesses affecting chilli crops. Our hybrid strategy seeks to improve illness classification models' performance and efficiency by using the advantages of both optimization methods. We conducted experiments on a comprehensive dataset co

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Pradnya L. Awate. "Crop Discrimination and Classification Using Sentinel-2A and Machine Learning Techniques." Journal of Information Systems Engineering and Management 10, no. 45s (2025): 1120–26. https://doi.org/10.52783/jisem.v10i45s.9139.

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Precision agriculture and agricultural monitoring rely heavily on accurate crop identification. This study examines the ability of Support Vector Machine and Random Forest classifiers to distinguish cotton, wheat, maize, and sugarcane using Sentinel-2 imagery. Both classifiers were trained and tested using ground truth data, with accuracy determined by the User's Accuracy, Producer's Accuracy, Overall Accuracy, and Kappa coefficient. The outcome revealed RF as having a better OA (83.33%) and Kappa value (0.77) than SVM (66.66% OA, 0.55 Kappa), reflecting a better classification performance. RF

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Badade, Prof Sonali. "A Deep Learning Based Framework for Accurate Prediction of Disease of Crops and Nutrients in Smart Agriculture." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 1879–82. http://dx.doi.org/10.22214/ijraset.2024.60198.

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Abstract: The health and production of crops have a major impact on societal well-being, and agriculture is essential to maintaining global food security. Crop output and quality are greatly impacted by nutrient shortages and crop diseases. We offer a unique approach to this problem that makes use of the Raspberry Pi, an inexpensive, small, and energy-efficient single-board computer, to forecast crop illnesses and identify nutrient deficits in real time. Our solution provides farmers and other agricultural stakeholders with fast and accurate information for efficient crop management by combini

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Sakkarvarthi, Gnanavel, Godfrey Winster Sathianesan, Vetri Selvan Murugan, Avulapalli Jayaram Reddy, Prabhu Jayagopal, and Mahmoud Elsisi. "Detection and Classification of Tomato Crop Disease Using Convolutional Neural Network." Electronics 11, no. 21 (2022): 3618. http://dx.doi.org/10.3390/electronics11213618.

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Deep learning is a cutting-edge image processing method that is still relatively new but produces reliable results. Leaf disease detection and categorization employ a variety of deep learning approaches. Tomatoes are one of the most popular vegetables and can be found in every kitchen in various forms, no matter the cuisine. After potato and sweet potato, it is the third most widely produced crop. The second-largest tomato grower in the world is India. However, many diseases affect the quality and quantity of tomato crops. This article discusses a deep-learning-based strategy for crop disease

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Thulasiraman, Hemanth, Aryan Jain, Tanmay Kukreja, and Rajkumar Rajasekaran. "Accessing the Efficiency of Machine learning Models for Crop Categorization Based on Regional Attributes in India." Procedia Computer Science 258 (2025): 2554–62. https://doi.org/10.1016/j.procs.2025.04.517.

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Sunitha, M., K. Manasa, Sravan Kumar G, B. Vijitha, and Sheik Farhana. "Ascertaining Along With Taxonomy of Vegetation Folio Ailment Employing CNN besides LVQ Algorithm." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6 (2023): 113–17. http://dx.doi.org/10.17762/ijritcc.v11i6.7278.

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In agriculture, early disease detection is crucial for increasing crop yield. The diseases Microbial Blotch, Late Blight, Septoria leaf spot, and yellow twisted leaves all have an impact on tomato crop productivity. Automatic plant illness classification systems can assist in taking action after ascertaining leaf disease symptoms. This paper emphasis on multi-classification of tomato crop illnesses employs Convolution Neural Network (CNN) model and Learning Vector Quantization (LVQ) algorithm-based methodology. The dataset includes 500 photographs of Tomato foliage with four clinical manifesta

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Balaji V, Purnendu Bikash Acharjee, Muniyandy Elangovan, Gauri Kalnoor, Ravi Rastogi, and Vishnu Patidar. "Developing a semantic framework for categorizing IoT agriculture sensor data: A machine learning and web semantics approach." Scientific Temper 14, no. 04 (2023): 1332–38. http://dx.doi.org/10.58414/scientifictemper.2023.14.4.40.

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This study introduces a Semantic Framework for Categorizing IoT Agriculture Sensor Data, leveraging Machine Learning and Web Semantics. IoT sensors in agriculture generate vast real-time data on crucial factors like soil conditions and weather, promising optimization in resource use and crop yields. While machine learning aids data categorization, semantic aspects often remain unexplored. By combining machine learning with web semantics (RDF and OWL), this research establishes a structured framework that not only categorizes data but also links it to actionable farming recommendations. Methodo

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Adebayo, Segun, Halleluyah Oluwatobi Aworinde, Akinwale O. Akinwunmi, Adebamiji Ayandiji, and Awoniran Olalekan Monsir. "Convolutional neural network-based crop disease detection model using transfer learning approach." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 1 (2023): 365–74. https://doi.org/10.11591/ijeecs.v29.i1.pp365-374.

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Crop diseases disrupt the crop's physiological constitution by affecting the crop's natural state. The physical recognition of the symptoms of the various diseases has largely been used to diagnose cassava infections. Every disease has a distinct set of symptoms that can be used to identify it. Early detection through physical identification, however, is quite difficult for a vast crop field. The use of electronic tools for illness identification then becomes necessary to promote early disease detection and control. Convolutional neural networks (CNN) were investigated in this study fo

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Umamaheswari, T. S. "Mathematical and Visual Comprehension of Convolutional Neural Network Model for Identifying Crop Diseases." International Academic Journal of Innovative Research 12, no. 1 (2025): 1–7. https://doi.org/10.71086/iajir/v12i1/iajir1201.

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Promptly detecting Crop Diseases (CD) mitigates adverse effects on plants. Convolutional Neural Networks (CNNs), especially Deep Learning (DL), are extensively used in computer vision (CV) and recognize patterns. Scholars have suggested several DL models for the detection of CD. Nonetheless, DL models need a substantial quantity of parameters, resulting in extended training durations and posing challenges for implementation on compact devices. This work discusses the Mathematical and Visual Comprehension of the CNN Model for Identifying Crop Diseases (MVC-CNN-CD). The inputs for image processi

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van Herk, Willem G., and Robert S. Vernon. "Categorization and numerical assessment of wireworm mobility over time following exposure to bifenthrin." Journal of Pest Science 86, no. 1 (2011): 115–23. http://dx.doi.org/10.1007/s10340-011-0381-2.

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V.S, Manjula, and Fatou Marega. "A review of soil analysis and classification to improve crops in agriculture using machine learning algorithm." Journal of Applied Science, Information and Computing 5, no. 1 (2024): 13–21. http://dx.doi.org/10.59568/jasic-2024-5-1-02.

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One of the most crucial aspects of our society is agriculture. For agriculture to be successful, soil is essential. Each type of soil has a unique composition. These chemical properties of the soil have an impact on crop growth. It is important to choose the right crop for that specific type of soil. Machine learning algorithms are able to classify the data from the soil series. to predict which crops are best suited to the soil type and climate of a certain area, the findings of this categorization can also be paired with crop datasets. Both the crop and the soil datasets are used. The files

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Sagaragara, Jennifer, and Melati Mediana Tobing. "Negosiasi Identitas Remaja Di Tiktok (Studi Fenomenologi Pada Content Creator Berpakaian Crop Top)." Journal of Innovative and Creativity (Joecy) 5, no. 2 (2025): 10466–77. https://doi.org/10.31004/joecy.v5i2.1537.

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Penelitian ini bertujuan untuk memahami bagaimana remajaperempuan membentuk dan menegosiasikan identitas sosialmereka melalui konten yang diunggah di TikTok, khususnyabagi mereka yang memilih mengenakan pakaian crop top. Dalam masyarakat Indonesia yang religius dan menjunjungtinggi nilai kesopanan, penggunaan crop top sering kali memicukontroversi. Namun, bagi sebagian remaja, crop top merupakansimbol kebebasan, ekspresi diri, dan keberanian menampilkanidentitas yang autentik. Menggunakan pendekatan kualitatifdengan metode fenomenologi transendental, penelitian inimenggali pengalaman subjektif

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Vilček, J. "Pedo-ecological categorization of Slovakia rural countryside with aspect to rye (Secale cereale L.) growing suitability." Agricultural Economics (Zemědělská ekonomika) 51, No. 4 (2012): 169–74. http://dx.doi.org/10.17221/5090-agricecon.

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The work objective is to differentiate rural land of Slovakia with aspect to the possibility of effective rye growing. The differentiation is based on pedo-climatic and production economic parameters. At soil categorization, correlation relationships between the site properties (soil and climatic conditions) and crop biological and agro-technological requirements were considered. Rye requirements were included into yield databases using the software filters in the way that the given site property excluded or limited rye growing, what was reflected in predicted production. The prediction was su

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Adebayo, Segun, Halleluyah Oluwatobi Aworinde, Akinwale O. Akinwunmi, Adebamiji Ayandiji, and Awoniran Olalekan Monsir. "Convolutional neural network-based crop disease detection model using transfer learning approach." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 1 (2022): 365. http://dx.doi.org/10.11591/ijeecs.v29.i1.pp365-374.

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Crop diseases disrupt the crop's physiological constitution by affecting the crop's natural state. The physical recognition of the symptoms of the various diseases has largely been used to diagnose cassava infections. Every disease has a distinct set of symptoms that can be used to identify it. Early detection through physical identification, however, is quite difficult for a vast crop field. The use of electronic tools for illness identification then becomes necessary to promote early disease detection and control. Convolutional neural networks (CNN) were investigated in this study for the el

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Reddy, Venkata Ravi Prakash, Harsh Kumar Dikshit, Gyan Prakash Mishra, et al. "Comparison of different selection traits for identification of phosphorus use efficient lines in mungbean." PeerJ 9 (October 8, 2021): e12156. http://dx.doi.org/10.7717/peerj.12156.

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Phosphorus (P) is one of the major constraints for crop growth and development, owing to low availability and least mobility in many tropical soil conditions. Categorization of existing germplasm under P deficient conditions is a prerequisite for the selection and development of P efficient genotypes in the mungbean. In the present investigation, 36 diverse genotypes were categorized for phosphorus use efficiency traits using four different techniques for identification of phosphorus use efficient mungbean genotypes. The studied genotypes were categorized for P efficiency based on efficiency,

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Rahu, Mushtaque Ahmed, Abdul Fattah Chandio, and Syed Mazhar Ali. "Machine Learning Overview in Agriculture." Journal of Applied Engineering & Technology (JAET) 6, no. 1 (2022): 28–39. http://dx.doi.org/10.55447/jaet.06.01.93.

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Today's agriculture industry makes extensive use of the promising field of machine learning (ML). There is not enough labour available for agriculture, and there are not enough skilled farmers. It is difficult to identify and stop crop diseases without a thorough understanding of the current situation. It is also frequently used in many aspects of agriculture, including managing soils, yields, water, diseases, and weather. The ML models allow rapid and actual decision-making. To anticipate correctness of the output, ML model uses training and testing. Species management, Disease detection, yie

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BOUDOUDA, Souheila, and Mountaha BOUKALLEL. "Intelligent Soil Classification for Precision Agriculture in Algeria." Algerian Journal of Signals and Systems 10, no. 2 (2025): 65–74. https://doi.org/10.51485/ajss.v10i2.264.

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Agriculture is a vital pillar of Algeria’s economic development and a fundamental contributor to the well-being of its population. The productivity of this sector largely depends on soil characteristics, which play a crucial role in determining suitable crop varieties. This paper presents a methodology for soil classification that considers both nutrient composition and physical attributes such as color and texture. By integrating data mining techniques with image classification algorithms, our approach aims to enhance the accuracy of soil type categorization. Image classification, in particul

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Kiruthika, S., and Dr D. Karthika. "Crop recommendation system and pest classification using weighted support vector machine on climate data." Salud, Ciencia y Tecnología - Serie de Conferencias 3 (May 12, 2024): 757. http://dx.doi.org/10.56294/sctconf2024757.

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Introduction: The primary cause of the significant decline in crop productivity is farmers' poor crop selection. A number of pests, including weeds, insects, plant diseases, and the poisonous nature of the most current remedies, offer challenges to the current approach. Therefore, for the most effective and precise classification and recommendations, these factors should be considered together.Methods: Levy flight Grey Wolf Optimization (LGWO) and the WSVM (Weight-Support Vector Machine) method are recommended in this research for the intention of upgrading the efficiency of the system as well

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A, Chitradevi, and Tajunisha N. "NORMALIZATION AND FEATURE SELECTION USING ENSEMBLE METHODS FOR CROP YIELD PREDICTION." ICTACT Journal on Soft Computing 14, no. 3 (2024): 3293–303. http://dx.doi.org/10.21917/ijsc.2024.0462.

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In machine learning study proposes an ensemble-based strategy for both feature selection and data standardization to enhance model performance and interpretability. To maintain consistency across datasets, it employ average filling and weighted K-means clustering. Weighted K-means assigns distinct values to samples based on their distances to cluster centers, offering a more precise representation of the data distribution. Meanwhile, average filling replaces missing values with the average of corresponding features, ensuring a complete dataset for subsequent analysis. For feature selection, ad

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Li, Yan, Guoxin Yao, Yafang Tang, Xudong Lu, Xiu Qiao, and Cheng Wang. "Genome-Wide Survey and Expression Analysis of the Basic Leucine Zipper (bZIP) Gene Family in Eggplant (Solanum melongena L.)." Horticulturae 8, no. 12 (2022): 1153. http://dx.doi.org/10.3390/horticulturae8121153.

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The transcription factors (TFs) family known as the basic leucine zipper (bZIP) plays a vital role in a variety of biological processes. However, there is no investigation on the bZIP family in the major vegetable crop, eggplant. Here, a total of 71 SmbZIP genes were identified from the eggplant genome and compared with other 18 representative plants. According to the topology of the phylogenetic tree, as well as the categorization and nomenclature of bZIP genes in Arabidopsis and Solanum lycopersicum, the SmbZIP family was classified into 13 groups. Analysis of the chromosome location, motif

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Srinivasaiah, Raghavendra, Meenakshi Meenakshi, Ravikumar Hodikehosalli Chennegowda, and Santosh Kumar Jankatti. "Analysis and prediction of seed quality using machine learning." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 5770. http://dx.doi.org/10.11591/ijece.v13i5.pp5770-5781.

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<span lang="EN-US">The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks an

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Srinivasaiah, Raghavendra, Meenakshi, Channegowda Ravikumar Hodikehosahally, and Jankatti Santosh Kumar. "Analysis and prediction of seed quality using machine learning." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 5770–81. https://doi.org/10.11591/ijece.v13i5.pp5770-5781.

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The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed

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Su, H. J., L. Y. Tsao, M. L. Wu, and T. H. Hung. "Biological and Molecular Categorization of Strains of Banana bunchy top virus." Journal of Phytopathology 151, no. 5 (2003): 290–96. http://dx.doi.org/10.1046/j.1439-0434.2003.00721.x.

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Shoaib, Muhammad. "Assessing the potential of Sentinel-2 imagery and NDVI thresholds for the development of crop phenology: A case study of Sahiwal District." Pakistan Journal of Agricultural Sciences 60, no. 03 (2023): 419–28. http://dx.doi.org/10.21162/pakjas/23.970.

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Crop mapping and its health monitoring are of key importance for a country like Pakistan where a considerable share of the GDP originates from agricultural production. Currently, extensive capital and human resource are utilized to record the crop types and monitor crop health due to variations in crop pattern and intercropping practices being observed in many parts of the country. Satellite Remote Sensing, however, can be utilized very efficiently to identify crop types and estimate acreage by using unique phenological cycles derived from NDVI thresholds. In this research, we proposed a semi-

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Venkadesh, S., R. Jagannathan, and K. P. Ragunath. "GIS BASED ANALYSIS OF SPATIAL DISTRIBUTION OF NDVI FOR AGRICULTURAL APPLICATIONS IN SALEM DISTRICT – TAMIL NADU." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 165–67. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-165-2019.

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<p><strong>Abstract.</strong> Remote sensing satellites in recent years have emerged as a vital tool for generating the biophysical information, which further helps to evolve the optimal land use plan for sustainable development of an area. The natural resources are to be categorized to obtain the area best suitable for crop production so that they could be better utilized in agricultural planning. The Normalized Difference Vegetation Index (NDVI) has been widely used to monitor moisture-related vegetation condition. The 8-day composite and spatial resolution of 250&amp;t

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Makhija, Himanshu, Rakesh Kumar Yadav, Anand Kopare, Lakhan Singh, and Amar Shankar. "Categorizing agricultural crops using unmanned aerial vehicle images: An in-depth review." Multidisciplinary Reviews 6 (April 29, 2024): 2023ss075. http://dx.doi.org/10.31893/multirev.2023ss075.

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Unmanned Aerial Vehicle (UAV) technology has been employed widely in the past several years to increase agricultural output while decreasing labor-intensive tasks, inspection times and crop management expenses. They can quickly cover enormous regions in which being utilized more often to gather important data for a variety of precision agricultural applications, such as crop/plant categorization. In particular, the use of UAVs for managing pressures such as water, illnesses, malnourishment and pests has been rising in regard to tracking and evaluating the health of plants, agriculture and wood

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Demestichas, Konstantinos, and Emmanouil Daskalakis. "Data Lifecycle Management in Precision Agriculture Supported by Information and Communication Technology." Agronomy 10, no. 11 (2020): 1648. http://dx.doi.org/10.3390/agronomy10111648.

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The role of agriculture in environmental degradation and climate change has been at the center of a long-lasting and controversial debate. This situation combined with the expected growth in crop demand and the increasing prices of fertilizers and pesticides has made the need for a more resource-efficient and environmentally sustainable agriculture more evident than ever. Precision agriculture (PA), as a relatively new farming management concept, aims to improve crop performance as well as to reduce the environmental footprint by utilizing information about the temporal and the spatial variabi

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Doorman, Frans. "Identifying Target Groups for Agricultural Research: The Categorization of Rice Farmers in the Dominican Republic." Experimental Agriculture 27, no. 3 (1991): 243–52. http://dx.doi.org/10.1017/s0014479700018962.

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SummaryA method is discussed for classifying small scale rice farmers from the Dominican Republic who have similar production systems and access to land, but differ widely in the yields they obtain and in the adoption of new technology. The results are used to define two recommendation domains, for farmers with ‘good’ and ‘poor’ production conditions, and to suggest appropriate technology for each. For farmers working in good production conditions the development of a technological package based on the double cropping of semi-dwarf varieties with high yield potential and production efficiency

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Okul Valentor, A., M. Ochwo-Ssemakula, T. Kaweesi, et al. "Plot based heritability estimates and categorization of cassava genotype response to cassava brown streak disease." Crop Protection 108 (June 2018): 39–46. http://dx.doi.org/10.1016/j.cropro.2018.02.008.

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VANEK, STEVEN J., and LAURIE E. DRINKWATER. "INTEGRATING SCIENTIFIC AND LOCAL SOILS KNOWLEDGE TO EXAMINE OPTIONS BY CONTEXT INTERACTIONS FOR PHOSPHORUS ADDITION TO LEGUMES IN AN ANDEAN AGROECOSYSTEM." Experimental Agriculture 55, S1 (2016): 145–68. http://dx.doi.org/10.1017/s0014479716000478.

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SUMMARYThis research sought to link Andean soil knowledge and farmer categorization of soil fertility to soil science characterization of soils, and use these to understand the impacts of phosphorus (P) fertilization of legumes using rock phosphate and soluble P fertilizer in 17 smallholder-managed sites with varying soil properties. We found that farmer high/low categorization of soils corresponded to soil P fertility and distance from farmer dwellings. Measures of soil P fertility also were inversely related to mycorrhizal colonization of vetch roots and directly related to the potential for

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Lv, Xinyao, Hao Xia, Na Li, Xudong Li, and Ruoming Lan. "MFVT: Multilevel Feature Fusion Vision Transformer and RAMix Data Augmentation for Fine-Grained Visual Categorization." Electronics 11, no. 21 (2022): 3552. http://dx.doi.org/10.3390/electronics11213552.

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The introduction and application of the Vision Transformer (ViT) has promoted the development of fine-grained visual categorization (FGVC). However, there are some problems when directly applying ViT to FGVC tasks. ViT only classifies using the class token in the last layer, ignoring the local and low-level features necessary for FGVC. We propose a ViT-based multilevel feature fusion transformer (MFVT) for FGVC tasks. In this framework, with reference to ViT, the backbone network adopts 12 layers of Transformer blocks, divides it into four stages, and adds multilevel feature fusion (MFF) betwe

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Smith, John Stephen C. "The Future of Essentially Derived Variety (EDV) Status: Predominantly More Explanations or Essential Change." Agronomy 11, no. 6 (2021): 1261. http://dx.doi.org/10.3390/agronomy11061261.

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This review examines the categorization of Essentially Derived Varieties (EDV) introduced in the 1991 revision of the Convention of the Union internationale pour la protection des obtentions végétales (UPOV). Other non-UPOV member countries (India, Malaysia, and Thailand) have also introduced the concept of essential derivation. China, a UPOV member operating under the 1978 Convention, is introducing EDVs via seed laws. Challenges in the implementation of the concept and progress made to provide greater clarity and more efficient implementation are reviewed, including in Australia and India. T

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Fosu-Mensah, Benedicta Y., Sarah Maku Adjovu, Ted Yemoh Annang, and Michael Mensah. "Assessment of farmers’ indigenous knowledge of soil quality management practices in Ghana: A case study of crop farmers in Ada West District." Journal of Applied and Natural Science 13, no. 3 (2021): 830–39. http://dx.doi.org/10.31018/jans.v13i3.2704.

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The efforts to increase soil productivity has been ?eld-based experiments with little information on farmers’ indigenous knowledge of soil quality acquired through experience. This study assessed farmers’ indigenous knowledge on soil quality and fertility management practices in the Ada West District of Ghana. Two hundred-and-twelve farmers from five communities were interviewed using pre-tested questionnaires. Fifteen farmers each from four communities identified and classified their soil into high, medium and low soil quality. Thirty-six soil samples were collected based on farmers’ categori

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Farhan, Sheikh Muhammad, Jianjun Yin, Zhijian Chen, and Muhammad Sohail Memon. "A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture." Sensors 24, no. 16 (2024): 5409. http://dx.doi.org/10.3390/s24165409.

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Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing

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Aziz, Tariq, Rahmatullah, M. Aamer Maqsood, M. Sabir, and S. Kanwal. "CATEGORIZATION OFBRASSICACULTIVARS FOR PHOSPHORUS ACQUISITION FROM PHOSPHATE ROCK ON BASIS OF GROWTH AND IONIC PARAMETERS." Journal of Plant Nutrition 34, no. 4 (2011): 522–33. http://dx.doi.org/10.1080/01904167.2011.538114.

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Shawon, Md Islamul Haque, Md. Abu Suphiyan, and Md. Salman Ferdous. "Land suitability Analysis for Sustainable Agriculture Development using Multi-Criteria Analysis and Machine Learning Techniques: Case Study of Shibpur Upazila." Journal of Tropical Resources and Sustainable Science (JTRSS) 13, no. 1 (2025): 138–48. https://doi.org/10.47253/jtrss.v13i1.1531.

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Assessing the suitability of crops is crucial for determining a plot of land's capacity for producing crops sustainably. The primary objectives of this study are to conduct a land suitability analysis for sustainable agricultural development and to develop a land information system with crop recommendations. In this study, land suitability categorization and crop recommendations were produced while taking into account many criteria such as slope, elevation, land cover, low flood risk, soil moisture, and favorable soil. Land suitability is categorized into three categories: most suitable, suita

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Thilmany, Elizabeth Anne, Serena Newton, Paul Goeringer, and Rachel E. Rosenberg Goldstein. "Reclaimed Water Use Regulations in the U.S.: Evaluating Changes and Regional Patterns in Patchwork State Policies from 2004–2023." Water 16, no. 2 (2024): 334. http://dx.doi.org/10.3390/w16020334.

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Water reuse, the beneficial use of highly treated municipal wastewater (reclaimed water), is expanding throughout the United States (U.S.); however, there are currently no federal reclaimed water use regulations, only guidelines. As a result, state policies on reclaimed water vary widely, emphasizing the need for a comprehensive understanding to facilitate coordinated national planning. Our systematic literature review, utilizing an online legal research database, presents an updated overview of U.S. reclaimed water policies from 2004 to 2023. A novel categorization scheme tracks policy change

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Pfau-Effinger, Birgit, and Marcel Sebastian. "Institutional persistence despite cultural change: a historical case study of the re-categorization of dogs in Germany." Agriculture and Human Values 39, no. 1 (2021): 473–85. http://dx.doi.org/10.1007/s10460-021-10272-4.

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AbstractHuman–animal relations in post-industrial societies are characterized by a system of cultural categories that distinguishes between different types of animals based on their function in human society, such as “farm animals” or “pets.” The system of cultural categories, and the allocation of animal species within this cultural classification system can change. Options for change include re-categorizing a specific animal species within the categorical system. The paper argues that attempts by political actors to adapt the institutional system to cultural change that calls for re-categori

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