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Journal articles on the topic 'Plant Disease Prediction Nepal'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Anam, Giri, Sapkota Rabin, Shrestha Raunak, Shrestha Prathama, Paudel Darshan, and Pokharel Ashwina. "Precision Farming in Nepal: A Machine Learning Perspective." International Journal of Environmental and Agriculture Research 10, no. 12 (2024): 68–72. https://doi.org/10.5281/zenodo.14598719.

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This paper encompasses three different machine learning models that we built to help Nepali farmers in selecting ideal crops for their land, using the right fertilizers, and predicting plant diseases. We tried about five models each for crop recommendation and fertilizer recommendation and a single model for plant disease prediction. We chose “Decision Trees” for both our Crop Recommendation and Fertilizer Recommendation and “Convolutional Neural Networks (CNN)” for Plant Disease Prediction. All models achieved over 95% accuracy. Our GitHub repository houses all the cod

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Monigari, Vaishnavi. "Plant Leaf Disease Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1295–305. http://dx.doi.org/10.22214/ijraset.2021.36582.

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The Indian economy relies heavily on agriculture productivity. A lot is at stake when a plant is struck with a disease that causes a significant loss in production, economic losses, and a reduction in the quality and quantity of agricultural products. It is crucial to identify plant diseases in order to prevent the loss of agricultural yield and quantity. Currently, more and more attention has been paid to plant diseases detection in monitoring the large acres of crops. Monitoring the health of the plants and detecting diseases is crucial for sustainable agriculture. Plant diseases are challen

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De Wolf, Erick D., and Scott A. Isard. "Disease Cycle Approach to Plant Disease Prediction." Annual Review of Phytopathology 45, no. 1 (2007): 203–20. http://dx.doi.org/10.1146/annurev.phyto.44.070505.143329.

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Kafle, Gandhiv, Indira Bhattarai (Sharma), Mohan Siwakoti, and Arjun Kumar Shrestha. "End uses of Kutki (Neopicrorhiza scrophulariiflora) in Nepal." Botanica Orientalis: Journal of Plant Science 14 (December 31, 2020): 39–42. https://doi.org/10.3126/botor.v14i1.80051.

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Kutki (Neopicrorhiza scrophulariiflora) is a medicinal herb found in the Himalayan region of Nepal. Government of Nepal has prioritized Kutki for research and economic development of the country. Understanding end uses of plant species is essential for prediction of future demand of the species and planning for its harvesting sustainability.

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Morgan, Maria, Carla Blank, and Raed Seetan. "Plant disease prediction using classification algorithms." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 257. http://dx.doi.org/10.11591/ijai.v10.i1.pp257-264.

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<p>This paper investigates the capability of six existing classification algorithms (Artificial Neural Network, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Decision Tree and Random Forest) in classifying and predicting diseases in soybean and mushroom datasets using datasets with numerical or categorical attributes. While many similar studies have been conducted on datasets of images to predict plant diseases, the main objective of this study is to suggest classification methods that can be used for disease classification and prediction in datasets that contain raw measureme

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Yuen, J. E., and G. Hughes. "Bayesian analysis of plant disease prediction." Plant Pathology 51, no. 4 (2002): 407–12. http://dx.doi.org/10.1046/j.0032-0862.2002.00741.x.

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Deshmukh, Mona, and Dr Girish Deshmukh. "Plant disease prediction using machine learning." International Journal of Engineering in Computer Science 4, no. 2 (2022): 15–18. http://dx.doi.org/10.33545/26633582.2022.v4.i2a.72.

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Waghulde, Isha, and Prof Nirmal Gaud. "Early Prediction of Plant Disease ESCA." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–11. http://dx.doi.org/10.55041/ijsrem28816.

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This research revolutionizes grapevine security worldwide and sustains premium wine production by using CNN-driven algorithms and different datasets to pioneer multimodal detection for early Esca disease in grapevines. Also give a model with more accuracy so that we can predict this plant disease early. Global grapevine output is being threatened by the complicated fungal illness known as esca disease, which also threatens the stability of the economy and the quality of premium wines. The capacity of current detection techniques to detect Esca to detect the disease early is restricted and freq

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Maria, Morgan, Blank Carla, and Seetan Raed. "Plant disease prediction using classification algorithms." International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 257–64. https://doi.org/10.11591/ijai.v10.i1.pp257-264.

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This paper investigates the capability of six existing classification algorithms (artificial neural network, naïve bayes, k-nearest neighbor, support vector machine, decision tree and random forest) in classifying and predicting diseases in soybean and mushroom datasets using datasets with numerical or categorical attributes. While many similar studies have been conducted on datasets of images to predict plant diseases, the main objective of this study is to suggest classification methods that can be used for disease classification and prediction in datasets that contain raw measurements

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Mutalik, Sumanth, Rashi, Rahamathunnisa, Rimsha, and Ms Chandana. "Crop Disease Prediction Using Web Application." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 840–45. http://dx.doi.org/10.22214/ijraset.2024.61696.

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Abstract: Agriculture plays a very vital role in our life. Without agriculture, the existence of human beings is not possible as it is the main source of our food supply to sustain on the earth and it also helps to grow our economy across the world. Plant disease detection is one of the most important aspects of maintaining an agriculturally developed nation. The timely and efficient detection of plant diseases is essential for a healthy and productive agricultural sector. Various diseases like Common Rust, Bacterial Spot, Leaf Mold, Mosaic Virus, Powdery Mildew and others that could affect a

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Scott, Anna, Janet Englund, Helen Chu, et al. "Meterology-driven Prediction of RSV/RHV Incidence in Rural Nepal." Open Forum Infectious Diseases 4, suppl_1 (2017): S360. http://dx.doi.org/10.1093/ofid/ofx163.873.

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Abstract Background Incidence of respiratory syncytial virus (RSV) and rhinovirus (RHV) varies throughout the year. We aim to quantify the relationship between weather variables (temperature, humidity, precipitation, and aerosol concentration) and disease incidence in order to quantify how outbreaks of RSV and RHV are related to seasonal or sub-seasonal meteorology, and if these relationships can predict viral outbreaks of RSV and RHV. Methods Health data were collected in a community-based, prospective randomized trial of maternal influenza immunization of pregnant women and their infants con

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Koju, R., R. Gurung, P. Pant, et al. "Prediction of Cardiovascular Disease in suburban population of 3 municipalities in Nepal." Nepalese Heart Journal 8, no. 1 (2013): 3–7. http://dx.doi.org/10.3126/njh.v8i1.8328.

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Background Cardiovascular disease (CVD) is a major cause of morbidity and mortality throughout the world including Nepal. The assessment of risk factors responsible for CVD can predict 10 year risk of CVD. World Health Organization (WHO) risk assessment chart, specially designed for South East Asia Sub-region D can predict the risk of CVD in Nepal. Method and Materials A total 532 study population from Panauti, Banepa and Dhulikhel were calculated their 10 year risk of CVD using WHO/ISH risk prediction chart on the basis of risk factors: age, gender, smoking status, diabetes, systolic blood pr

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Adhikari, Raj K., Punya P. Regmi, Resham B. Thapa, Yubak D. GC, and Eric Boa. "Impact of Plant Clinics on Farmers’ Knowledge, Attitude and Practice With Plant Health Issues." Turkish Journal of Agriculture - Food Science and Technology 7, no. 9 (2019): 1490. http://dx.doi.org/10.24925/turjaf.v7i9.1490-1496.1836.

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Plant clinics in Nepal has been started in 2008 and has coverage in more than 40 districts of Nepal. A study has been conducted in Chitwan district of Nepal to assess the changes on farmer’s knowledge attitude and practice due to implementation of plant clinics. Total of 175 clinic attendants and 175 non-attendants were selected by simple random sampling method and interviewed by using semi-structured questionnaire. By and large, this study found wider positive changes among clinic attendants than non-attendants especially on their knowledge and skills to identity pests and diseases with their

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Prem, G., M. Hema, Laharika Basava, and Anjali Mathur. "Plant Disease Prediction using Machine Learning Algorithms." International Journal of Computer Applications 182, no. 25 (2018): 1–7. http://dx.doi.org/10.5120/ijca2018918049.

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M S, Hema, †, Niteesha Sharma, Y Sowjanya, Ch. Santoshini, R Sri Durga, and V. Akhila. "Plant disease prediction using convolutional neural network." EMITTER International Journal of Engineering Technology 9, no. 2 (2021): 283–93. http://dx.doi.org/10.24003/emitter.v9i2.640.

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Every year India losses the significant amount of annual crop yield due to unidentified plant diseases. The traditional method of disease detection is manual examination by either farmers or experts, which may be time-consuming and inaccurate. It is proving infeasible for many small and medium-sized farms around the world. To mitigate this issue, computer aided disease recognition model is proposed. It uses leaf image classification with the help of deep convolutional networks. In this paper, VGG16 and Resnet34 CNN was proposed to detect the plant disease. It has three processing steps namely

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Saha, Pratiti, and Dr Nachappa M N. "Cotton Plant Disease Prediction Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 744–46. http://dx.doi.org/10.22214/ijraset.2022.40731.

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Abstract: The use of deep learning models to identify lessions on cotton leaves on the basis of images of the crop in the field is proposed in this article. Its cultivation in tropical regions has made it the target of a wide spectrum of agricultural pests and diseases, and efficient solutions are required. Moreover, the symptoms of the main pests and diseases cannot be differentiated in the initial stages, and the correct identification of a lesion can be difficult for the producer. To help resolve the problem, the present research provides a solution based on deep learning in the screening o

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Ashritha, K., K. Sandhya, Y. Uday Kiran, and V. N. L. N. Murthy. "Plant-Leaf Disease Prediction Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 121–28. http://dx.doi.org/10.22214/ijraset.2023.49338.

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Abstract: Brown spot, Mosaic, Grey spot, and Rust all significantly reduce apple yield. Rust is a sign of Foliar illness in this instance. The primary factor influencing apple output is the occurrence of apple leaf diseases, which results in significant yearly economic losses. Therefore, it is very important to research apple leaf disease identification. Plants are frequently attacked by pests, bacterial diseases, and other microorganisms. Inspection of the leaves, stem, or fruit usually identifies the attack's signs. Powdery Mildew and Leaf Blight are two common plant diseases that can cause

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Waghmare, Urvashi. "Plant Diseases Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 10 (2024): 1459–64. http://dx.doi.org/10.22214/ijraset.2024.64917.

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The early detection and accurate prediction of plant diseases are crucial for improving crop health and maximizing agricultural productivity. Traditional methods of disease detection, which rely heavily on manual observation, are often timeconsuming, labor-intensive, and prone to human error. Recent advancements in machine learning (ML) have opened new possibilities for developing efficient, automated systems that can predict plant diseases with high accuracy. This paper explores various machine learning techniques, including supervised and deep learning models, for plant disease prediction. B

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Yuen, Jonathan. "Bayesian Approaches to Plant Disease Forecasting." Plant Health Progress 4, no. 1 (2003): 20. http://dx.doi.org/10.1094/php-2003-1113-06-rv.

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Prediction of disease occurrence is a well known historical theme, and has begun to receive new interest due to internet-based prediction systems. The evaluation of these systems in a quantitative manner is an important step if they are to be used in modern agricultural production. Bayes's theorem is one way in which the performance of such predictors can be studied. In this way, the conditional probability of pest occurrence after a positive or negative prediction can be compared with the unconditional probability of pest occurrence. Both the specificity and the sensitivity of the predictive

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20

Mohan, Dr K. Madan. "LEAF DISEASE PREDICTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–6. http://dx.doi.org/10.55041/ijsrem27703.

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Leaf diseases are a major problem in agriculture, causing significant losses in crop yield and quality. Early detection of leaf diseases is essential for effective management, but it can be difficult and time- consuming to do manually. In recent years, there has been growing interest in the use of machine learning and computer vision techniques for leaf disease prediction. These techniques can be used to automatically extract features from leaf images that are indicative of disease, and then use these features to train a classifier that can distinguish between healthy and diseased leaves. Seve

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Vanlalhruaizela, Barnaba, Lalremsangi, Loicy Lalrinnungi, Pragnya N, and Vinutha M. S. "Plant Disease Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 4925–30. http://dx.doi.org/10.22214/ijraset.2024.62750.

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Abstract: This project addresses the critical challenge of plant disease detection in India, a nation where agriculture supports nearly 70% of the population. Traditional procedures for identifying plant diseases are both labor-intensive and time-consuming. To enhance efficiency and accuracy, this project introduces an innovative approach that integrates machine learning and image processing techniques. By analyzing images of leaves, the system identifies diseases based on key factors such as leaf color, damage extent, area, and texture parameters. Unlike conventional methods that depend on vi

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Chaurasia, PCP, and E. Duveiller. "Management of Leaf Blight (Bipolaris sorokiniana) Disease of Wheat with Cultural Practices." Nepal Agriculture Research Journal 7 (May 22, 2009): 63–69. http://dx.doi.org/10.3126/narj.v7i0.1870.

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An experiment was conducted at agronomy farm of Agriculture research Station, Tarahara,Nepal for three consecutive years viz 2000/2001,2001/2002, and 2002/2003 to evaluate theeffects of different cultural practices on leaf blight diseases of wheat caused by Bipolarissorokiniana under terai conditions. The experiment was conducted in factorial RandomizedComplete Block Design with four replications. Four factors, two wheat verities RR-21 andNepal 297,two number of irrigations (two and three irrigations), two doses of Nitrogen fertilizerviz. 60kg N/ha and 120 kg N/ha and two dates of sowing of wh

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Bala Murugan, MS, Manoj Kumar Rajagopal, and Diproop Roy. "IoT Based Smart Agriculture and Plant Disease Prediction." Journal of Physics: Conference Series 2115, no. 1 (2021): 012017. http://dx.doi.org/10.1088/1742-6596/2115/1/012017.

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Abstract Agriculture is a very prominent sector in our country and has been one of the highest contributors to the GDP. During the 1960s, an all-time high was reached with approximately 50% contribution to the GDP of the country, as more than half of the population was rural and focused primarily on agriculture as means of their livelihood. But from the latest records of 2019, the contribution by this sector has decreased to 15.96 percent. IoT plays a significant tole in remote sensing with machine learning in monitoring crops and surveying, which in turn aids agriculturists in ways for effici

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Mäyrä, Outi, Mika Ruusunen, Marja Jalli, Lauri Jauhiainen, and Kauko Leiviskä. "Plant Disease Outbreak – Prediction by Advanced Data Analysis." SNE Simulation Notes Europe 28, no. 3 (2018): 113–15. http://dx.doi.org/10.11128/sne.28.sn.10431.

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Gulame, Mayuresh B., Trupti G. Thite, and Kranti D. Patil. "Plant disease prediction system using advance computational Technique." Journal of Physics: Conference Series 2601, no. 1 (2023): 012031. http://dx.doi.org/10.1088/1742-6596/2601/1/012031.

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Abstract A vital sector of India’s economy is agriculture. Identification of plant infections is crucial to preventing crop damage and further disease. The majority of plants, such as apple, tomato, cherry, and grapes, have leaves that appear to have disease signs. The plant health can be monitored through images to precisely predict the disease and to take early preventative action. The traditional method is to manually inspect the plant leaf to identify the kind of disease, as done by farmers or plant pathologists. In this research, we presented a deep CNN model termed as Decompose, Transfer

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Pokhrel, A. K., H. P. Adhikari, U. Joshi, B. R. Tiwari, B. R. KC, and K. N. Poudyal. "Prediction of Solar Radiation using Empirical Models over Lowland Region Nepal." Journal of Nepal Physical Society 9, no. 1 (2023): 51–57. http://dx.doi.org/10.3126/jnphyssoc.v9i1.57598.

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Renewable energy sources are becoming more and more popular as a result of the world's rapidly rising energy needs and the depletion of fossil fuel stocks. Solar energy significantly increases the need for electricity. Due to high energy demands, solar energy most viable out among all of energy sources. Before choosing a location for a solar-powered power plant, it is necessary to predict or anticipate solar energy. For the estimation of the solar energy we use meteorological parameters such as sunshine hour, temperature and relative humidity on selected empirical models to find the empirical

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Timila, R. D., J. C. Correll, and V. R. Duwadi. "Severe and Widespread Clubroot Epidemics in Nepal." Plant Disease 92, no. 2 (2008): 317. http://dx.doi.org/10.1094/pdis-92-2-0317b.

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Cultivation of brassica vegetables has the highest potential for generating income among more traditional rice and maize farmers in Nepal. Among brassica vegetables, the most important are cauliflower (Brassica oleracea var. botrytis L.) and cabbage (B. oleracea var. capitata L.). Although clubroot disease, caused by Plasmodiophora brassicae Woronin, has been observed in Nepal since 1993, severe and widespread epidemics have been observed since 2004 in the Bhaktapur, Kathmandu, Lalitpur, and Palung Valley production areas. Typical disease symptoms (1) are widespread, and disease severity has b

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Acharya, Sanskriti. "DUAL CULTURE APPROACH FOR THE EVALUATION OF ANTAGONISTIC ACTIVITY OF NATIVE TRICHODERMA ISOLATES AGAINST BIPOLARIS SOROKINIANA." International Journal of Advanced Research 9, no. 12 (2021): 1002–8. http://dx.doi.org/10.21474/ijar01/14000.

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Spot blotch disease caused by Bipolaris sorokiniana (Sacc.) shoem. is one of the most destructive diseases of wheat (Triticum aestivum L.) in plain areas of Nepal. A laboratory experiment was conducted to evaluate the antagonistic activity of seven test Trichoderma isolates obtained from the soil sample by employing dual culture method against spot blotch pathogen of wheat Bipolaris sorokiniana at Plant Pathology Division, Nepal Agriculture Research Council, Khumaltar, Nepal. The experiment was laid out in a Completely Randomized Design (CRD). The result revealed that all Trichoderma isolates

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Shinde, Nirmala, and Dr Guddi Singh. "A Review of Plant Disease Prediction Methods for Agricultural Applications." International Journal of Engineering and Advanced Technology 12, no. 1 (2022): 98–103. http://dx.doi.org/10.35940/ijeat.a3856.1012122.

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Due to the decrease in plant quality and productivity, plant diseases seem to be responsible for significant economic losses in the world. As a result, farmers nowadays consider plant disease prediction to be an important area of research. To help an accurate prediction of plant disease, numerous techniques have been detailed in the literature. To highlight the many issues with current approaches for problem-solving predictions, we will evaluate various literary works that are focused on plant disease prediction in the agricultural industry. Based on several variables, including different data

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Nirmala, Shinde, and Guddi Singh Dr. "A Review of Plant Disease Prediction Methods for Agricultural Applications." International Journal of Engineering and Advanced Technology (IJEAT) 12, no. 1 (2022): 98–103. https://doi.org/10.35940/ijeat.A3856.1012122.

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<strong>Abstract:</strong> Due to the decrease in plant quality and productivity, plant diseases seem to be responsible for significant economic losses in the world. As a result, farmers nowadays consider plant disease prediction to be an important area of research. To help an accurate prediction of plant disease, numerous techniques have been detailed in the literature. To highlight the many issues with current approaches for problem-solving predictions, we will evaluate various literary works that are focused on plant disease prediction in the agricultural industry. Based on several variable

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Parimala, Mrs M. "Harvestify Crop Disease Prediction and Recommendation." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50363.

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Abstract— As the primary source of income for most Indians, agriculture is a major economic sector in the country. With machine learning (ML) techniques, Harvestify aims to improve farming practices. This research includes the development of a machine learning (ML) system that assists farmers in predicting the optimal harvest times and recommending crops that are suitable for the soil and climate of their area. It also detects and treats plant issues using photo recognition. Harvestify also includes a Soil-depending Profiling System that analyzes data to select crops depending on soil characte

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Shrestha, Surendra Lal, Suprabha Pandey, Yadav Kumar Shrestha, and Iswori Prasad Gautam. "Evaluation of Broad Leaf Mustard Genotypes in Central Mid-hill of Nepal." Journal of Nepal Agricultural Research Council 7 (April 30, 2021): 151–60. http://dx.doi.org/10.3126/jnarc.v7i1.36941.

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Seven genotypes of broad leaf mustard were evaluated for their performance at research field of Khumaltar, Lalitpur, Nepal in winter season of consecutive three years; 2016, 2017 and 2018. The main objective of these studies was to find out the most promising genotypes of broad leaf mustard. The experiments were conducted in randomized complete block design with three replications. Three weeks old seedlings were transplanted on first week of October at the spacing of 45 cm x 30 cm. Fertilizer was applied at the rate of 200:150:100 NPK kg/ha and 15ton compost per hectare and other cultural prac

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R, Sumanth. "AI-Driven Crop Disease Prediction and Management System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47236.

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Abstract—The agricultural sector faces critical challenges due to plant diseases, leading to reduced crop yields, economic losses, and food insecurity. Traditional plant disease detection methods are based on manual inspection, which is time consuming, subjec- tive, and prone to errors. This research presents an AI-powered system that utilizes deep learning, specifically Convolutional Neural Networks (CNNs), for efficient disease identification. The model processes plant leaf images to extract key features, classify diseases, and provide real-time predictions. Integrated with a web-based appli

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Chaudhary, Sujan, Gyanu Thapa Magar, Shiv Nandan Sah, and Sanju Parajuli. "Ethnic Plants of Tharu Community of Eastern Nepal." International Journal of Applied Sciences and Biotechnology 8, no. 2 (2020): 223–30. http://dx.doi.org/10.3126/ijasbt.v8i2.28325.

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Ethnobotany is the study of ethnic plants of particular people living in particular place. It accommodates list, details and description of all those studied plants regarding with their various uses. Tharu people belong to one of the indigenous communities especially in Terai region of Nepal. Tharu people have been using these plant resources since 18th century. This study aims to identify ethnic plants of Tharu community of eastern Nepal and their uses in their daily lives. Enumeration of useful plants from Tharu ethnic community was conducted in four villages of Ramdhuni municipality of Suns

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Basnet, Roshan, Sundar Man Shrestha, Deepak Bandari, Hira Kaji Manandhar, and Dhruba Bahadur Thapa. "Evaluation of wheat (Triticum aestivum L.) genotypes for spot blotch (Bipolaris sorokiniana Sacc) resistance in terai condition of Nepal." Archives of Agriculture and Environmental Science 7, no. 3 (2022): 440–49. http://dx.doi.org/10.26832/24566632.2022.0703019.

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Spot blotch caused by Bipolaris sorokiniana is a major disease of wheat in warm and humid regions of Nepal. The fungus has a worldwide distribution but as a pathogen, it is the most aggressive under the conditions of high relative humidity and temperature associated with the low fertility of soils in Nepal. The yield loss due to the disease is very significant in Nepal. This experiment was conducted to identify the genotypes having a good level of resistance against spot blotch. The experiment set was received from CIMMYT comprises 52 genotypes and arranged in alpha lattice design with two rep

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Basnet, Roshan, Sundar Man Shrestha, and Deepak Bandari. "Performance of Wheat (Triticum aestivum L.) Genotypes for Spot Blotch (Bipolaris sorokiniana Sacc.) Resistance in Terai Condition of Nepal." International Journal of Research and Review 9, no. 8 (2022): 894–906. http://dx.doi.org/10.52403/ijrr.20220879.

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Spot blotch caused by Bipolaris sorokiniana is a major disease of wheat in warm and humid regions of Nepal. The fungus has a worldwide distribution but as a pathogen, it is the most aggressive under the conditions of high relative humidity and temperature associated with the low fertility of soils in Nepal. The yield loss due to the disease is very significant in Nepal. This experiment was conducted to identify the genotypes having a good level of resistance against spot blotch. The experiment set was received from CIMMYT comprises 52 entries and arranged in alpha lattice design with two repli

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Manjiri, U. Karande, S. L. Satarkar Dr., and A. Kharche N. "Smart Agriculture and Plant Disease Prediction System- An overview." International Organization of Research & Development (IORD) 9, no. 2 (2022): 4. https://doi.org/10.5281/zenodo.6331515.

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<em>Agriculture acts as the basic stamina of a country, contributing more than half of the sector of the economy throughout the world. The requirement for precision agriculture is important in determining the states of the crops to find the proper treatment for the plant. Like human beings and animals, plants do suffer from diseases that affect the complete plant, including leaf, stem, fruit, root, and flower and as a result, the plant may die or cause leaf drop, flowers, fruit drop, etc. For that purpose, the appropriate diagnosis is essential for the accurate identification and treatment of

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N S, Mrs Shylaja, Bhoomika T S, Brunda S V, and Rachana K S. "Deep Learning Model for Early Prediction of Plant Disease." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 4415–19. http://dx.doi.org/10.22214/ijraset.2022.46008.

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Abstract: Recognition of illnesses in the agricultural flora is a essential which needs to be executed in farming. This is approximate on which the financial system greatly depends. Infections finding in flora is sizable work in the agriculture business arena, as consuming ailments in plants is very common. To identify the disease in Leaves, constant statement of a plant life is compulsory. The statement and non-stop monitoring of the flora takes a lot of human effort and it is tedious. And clearly some kind of programmed strategy is required to study the plants. Database based documentation o

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Patil, Rutuja Rajendra, Sumit Kumar, and Ruchi Rani. "Comparison of Artificial Intelligence Algorithms in Plant Disease Prediction." Revue d'Intelligence Artificielle 36, no. 2 (2022): 185–93. http://dx.doi.org/10.18280/ria.360202.

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The оссurrenсe or сhаnge in the diseases in а specific аreа саn be рrediсted in аdvаnсe with the help оf рlаnt disease fоreсаsting model. This helps to undertake suitable management measures to аvоid the losses well in аdvаnсe. Disease forecasting рrediсts рrоbаble outbreaks or increased disease intensity over a period in a particular area. This technique helps in timely аррliсаtiоn оf сhemiсаls to рlаnts, which also involve all асtivities оf сrор protection and intimate the farmers in the community via text messages or e-mail etс. means оf соmmuniсаtiоn. Environment controls the evolution and

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Wilks, Daniel S., and Karin W. Shen. "Threshold Relative Humidity Duration Forecasts for Plant Disease Prediction." Journal of Applied Meteorology 30, no. 4 (1991): 463–77. http://dx.doi.org/10.1175/1520-0450(1991)030<0463:trhdff>2.0.co;2.

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SENGALIAPPAN, Dr M. "Plant Disease Prediction and Farmers Assistance Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42171.

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Agriculture plays a crucial role in sustaining economies and food security worldwide. In the context of Digital India, leveraging technology for farming can significantly enhance efficiency and productivity. This paper presents a comprehensive agricultural application designed to assist farmers in multiple domains. The platform offers features such as plant and disease management, enabling early detection and mitigation of crop diseases to minimize economic losses. Additionally, a marketplace facilitates the buying and selling of farming equipment and tools, ensuring fair pricing and easy acce

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Saxena, Niharika, and Dr Neha Sharma. "IDENTIFICATION OF TOMATO LEAF DISEASE PREDICTION USING CNN." International Journal of Engineering Science Technologies 6, no. 5 (2022): 46–58. http://dx.doi.org/10.29121/ijoest.v6.i5.2022.397.

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In India tomatoes are broadly vegetable crop. However, the tropical environment is ideal for tomato plant growth, specific climatic conditions and other factors influence tomato plant growth. Aside from these environmental factors and natural disasters, plant disease is a serious agricultural production issue that causes economic loss. As an outcome, early illness detection may produce better results than existing detection methods. As a result, deep learning approaches based on computer vision might be used to detect diseases early. The disease categorization and detection strategies used to

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Saxena, Niharika, and Neha Sharma. "TOMATO LEAF DISEASE PREDICTION USING TRANSFER LEARNING." International Journal of Engineering Technologies and Management Research 9, no. 6 (2022): 1–14. http://dx.doi.org/10.29121/ijetmr.v9.i6.2022.1177.

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Tomatoes are the most extensively planted vegetable crop in India's agricultural lands. Although the tropical environment is favorable for its growth, specific climatic conditions and other variables influence tomato plant growth. In addition to these environmental circumstances and natural disasters, plant disease is a severe agricultural production issue that results in economic loss. Therefore, early illness detection can provide better outcomes than current detection algorithms. As a result, deep learning approaches based on computer vision might be used to detect diseases early. This stud

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Kafle, Gandhiv, Indira Bhattarai (Sharma), Mohan Siwakoti, and Arjun Kumar Shrestha. "Demand, End-Uses, and Conservation of Alpine Medicinal Plant Neopicrorhiza scrophulariiflora (Pennell) D. Y. Hong in Central Himalaya." Evidence-Based Complementary and Alternative Medicine 2018 (June 19, 2018): 1–12. http://dx.doi.org/10.1155/2018/6024263.

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Neopicrorhiza scrophulariiflora (Pennell) D. Y. Hong of Scrophulariaceae family (hereinafter referred to as Neopicrorhiza) has medicinally important rhizomes with high levels of trade. What factors drive demand for Neopicrorhiza in Central Himalaya is unknown. In this context, a nationwide comprehensive survey was conducted from September 2016 to March 2017 to assess demand, end-uses, and conservation of dry Neopicrorhiza rhizomes in Nepal. A total of 2313 herbal products were surveyed for Neopicrorhiza as an ingredient in 38 retailer shops. Processing industries of Neopicrorhiza in Nepal were

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Basnet, Roshan, Laxman Aryal, and Biswash Raj Bastola. "Spot blotch disease resistance and heat stress tolerance in spring wheat (Triticum aestivum L.)." Archives of Agriculture and Environmental Science 8, no. 1 (2023): 20–27. http://dx.doi.org/10.26832/24566632.2023.080104.

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Spot blotch caused by Bipolaris sorokiniana is a major disease of wheat in warm and humid regions of Nepal. The fungus has a worldwide distribution but as a pathogen, it is the most aggressive under the conditions of high relative humidity and temperature associated with the low fertility of soils in Nepal. The yield loss due to the disease is very significant in Nepal. This experiment was conducted to identify the genotypes having a good level of resistance against spot blotch. Canopy temperature measurements using infrared thermometry, to assess variation in foliar blight resistance along wi

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Sankhe, Smita R., and Asha Ambhaikar. "Plant disease detection and classification techniques: A review." Multiagent and Grid Systems: An International Journal of Data Science and Artificial Intelligence 20, no. 3-4 (2024): 265–82. https://doi.org/10.1177/15741702241304087.

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Crop production can be greatly affected by different diseases, which seriously threaten food security. Consequently, detecting plant diseases earlier and preventing the spread of plant diseases is necessary to avoid the economic imbalance. Nevertheless, the manual detection of plant diseases is a time-consuming and error-prone process. Numerous existing techniques are adopted that are unreliable in terms of accuracy and fail to identify the infected region due to non-uniform complex backgrounds resulting in mispredictions. Hence, this research presents a review that focuses on enhancing plant

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Thapa, Lal Bahadur, Til Maya Dhakal, and Sanjay Kumar Jha. "Traditional method of disease classification, and identification of medicinal plants and mushrooms by Raji ethnic people in western Nepal." Botanica Orientalis: Journal of Plant Science 13 (December 31, 2019): 22–28. https://doi.org/10.3126/botor.v13i1.80042.

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Rajis are indigenous people distributed in mid and far western parts of Nepal. They have immense knowledge and traditional practices on utilization of plant resources as food and medicine. The present study was conducted in Surkhet and Kailali districts of western Nepal to document the Raji’s knowledge on medicinal plants and mushrooms. A total of 57 species of vascular plants belonging to 50 genera and 33 families were reported as the medicinal plants traditionally being utilized by the Raji people. They have classified the human diseases into six categories according to various symptoms. Tra

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Shrestha, SurendraLal, Mira Dhakal, Sangeeta Sapkota, Ishwori Prasad Gautam, Suprabha Pandey, and Yadav Shrestha. "Evaluation of Zucchini (Cucurbita pepo L.) Genotypes for Spring Season Production in the Mid–Hills of Nepal." Nepal Journal of Science and Technology 20, no. 1 (2021): 73–81. http://dx.doi.org/10.3126/njst.v20i1.43356.

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Zucchini (Cucurbita pepo L.) is one of the most popular and demanded vegetable in Nepal. Seedlings of seven genotypes of squash gourd; Super Squash Ball, Squash Long, Squash Light, Squash Green Ball, Squash Green House Sondo V (check) and Grey Zucchini (check) were transplanted on second week of March in two consecutive years; 2016 and 2017 in field condition at Khumaltar with a spacing of 90 x 90 cm in four replications. The standard recommended dose of fertilizers (240:180:60 NPK kg/ha + 15 ton FYM/ha) and minimum pesticide and fungicide was applied. The main objective of this experiment was

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A. SRI LAKSHMI and JYOTHI N M. "Prediction of Plant Disease Severity Using Advanced Gradient Boosting Techniques." Bioscan 19, Special Issue-1 (2024): 886–88. https://doi.org/10.63001/tbs.2024.v19.i02.s.i(1).pp886-888.

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Plant diseases significantly impact agricultural productivity, causing economic losses and food insecurity. Predicting the severity of plant diseases is crucial for timely interventions and sustainable farming practices. However, existing methods often lack precision and scalability. This study aims to develop a highly accurate model to predict plant disease severity using advanced gradient boosting techniques. We used the publicly available New Plant Diseases Dataset from Kaggle, containing images of healthy and diseased plants. Data preprocessing included image augmentation and feature extra

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Nachankar, Abhishek P., Achal Radheshyam Ganvir, Shweta Raviji Yesambare, Tanuja Namdeo Fule, Sneha Diwakar Surjuse, and Pradunya Rajendra Rangari. "Cotton Leaf Disease Prediction Using Transfer Learning." International Journal of Computer Science and Mobile Computing 11, no. 2 (2022): 136–42. http://dx.doi.org/10.47760/ijcsmc.2022.v11i02.017.

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Cotton is one of the financially significant agricultural items in India, but it is exposed to different constraints in the leaf area. Mostly, these constraints are identified as diseases that are hard to detect with bare eyes. This study focused to develop a model to boost the detection of cotton leaf disease and pests using the deep learning technique. Basically here we did comparative study of own defined convolution neural network architecture and popular state of art CNN architecture. This study centered to foster a model to classify diseased and fresh Cotton plant and leaf using Deep Lea

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