Baliraja: A Crop Recommendation And Risk Alerting System
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This document presents Baliraja, a crop recommendation and risk alerting system. It discusses the problems farmers face like heat, rains, pests and proposes an objective to build a system using soil, weather and satellite data to recommend crops and alert farmers of risks. It describes the methodology which includes collecting datasets, applying algorithms like CNN, SVM using AIOT for feature extraction to generate recommendations and alerts to train and test the system. The scope and innovations using AIOT for Baliraja are highlighted. The applications are providing weather-based crop recommendations and crop risk alerts as well as pesticide management. It concludes with references for related work.
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reducing waste while improving output. With IoT-based smart farming, farmers may better manage their animals, develop crops, save costs, and
conserve resources. Climate monitoring, drought detection, agriculture and production, pollution distribution, and many more applications rely on the weather forecast. The accuracy of the forecast is determined by prior
weather conditions across broad areas and over long periods. Machine learning algorithms can help us to build a model with proper accuracy. As a result, increasing the output on the limited acreage is important. IoT smart farming is a high-tech method that allows people to cultivate crops cleanly
and sustainably. In agriculture, it is the use of current information and
communication technologies.IRJET - Enlightening Farmers on Crop Yield
This document discusses using data mining techniques to predict crop yields to help farmers. It proposes using a random forest regression algorithm on past agricultural data from 2000-2014 to build a prediction model. The model would help farmers select optimal crops, understand weather patterns, and maximize yields. The system is described as gathering data, preprocessing it, training a random forest model on 60% of the data and testing it on 20%. It would then provide yield predictions and recommendations to farmers through a visualization tool. The goal is to help guide farmers' decisions around fertilizer use, soil management, and crop selection to improve production levels.
“Plant Disease Detection by Using Deep LearningAlgorithm with Product, Price ...
This document summarizes a research paper that proposes using deep learning algorithms like convolutional neural networks (CNNs) to detect plant diseases, recommend products and prices, and predict crops. The system is designed to help farmers by identifying diseases from images, providing treatment options and costs, and recommending suitable crops for their soil conditions and location based on parameters like temperature, humidity and moisture. It describes collecting seed data, training a CNN model on a plant disease image dataset, and using neural networks to identify disease colors and textures for diagnosis. The system aims to automate crop yield prediction and guide new farmers in India by deploying machine learning technologies.
M044066366
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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Baliraja: A Crop Recommendation And Risk Alerting System
- 1. Baliraja: A Crop Recommendation And Risk Alerting System Presented By-Bhavana Kudkyal. JSPM's Jayawantrao Sawant College of Engineering, Pune
- 3. Problem Statement Time Scorching Heat Rains Shock Hazards Night Visit Labourers Distance Crops Soil
- 4. Objective of Project Crop Recommendation and Risk Alerting System Soil Weather Temperature Wind Speed w Satellite
- 5. Project Methodology Dataset Collection Algorithms Applied(AIOT) CNN SVM IPM Image processing Recommendation And Alerting Sysyem Feature Extraction Recommended crops and Risk Alerted Tranining Databse Dataset Testing
- 7. Innovation In The Project AIOT in BaliRaja
- 8. Application of The Project Weather Condition Crop Recommendation Crop Risk Alerting Pesticides Management
- 9. References [1] L. Madhusudhan, “Agriculture Role on Indian Economy”, Business and Economics Journal, 2015. [2] V. Kumar, V. Dave, R. Bhadauriya, S. Chaudhary, “KrishiMantra: Agricultural Recommendation System”, ACM Symposium on Computing for Development, Bangalore, India, 2013, pp. 1-2. [3] SAVE FOOD: Global Initiative on Food Loss and Waste reduction. http://www.fao.org/save-food/resources/keyfindings/en/. [4] P. S. Vijayabaskar, R. Sreemathi, E. Keertanaa, “Crop Prediction using Predictive Analytics”, IEEE International Conference on Computationof Power, Energy Information and Communication,Melmaruvathur,India, 2017, pp. 370 373. [5] J. Lacasta, F. J. Lopez-Pellicer, B. Espejo-Garcia, J. Nogueras-Iso, F. J. Zarazaga-Soria, “Agriculture Recommendation System for Crop Protection”, Computers and Electronic in Agriculture, Elsevier, vol. [6] S. Kanaga Subh Raja, R. Rishi, E. Sundaresan, V. Srijit, “Demand based Crop Recommender System for Farmers”, IEEE Technological Innovations in ICT for Agriculture and Rural Development, Chennai, India, 2017, pp. 1
- 10. Thank You !! For Your Attention Any Questions?