International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
BHARATH KISAN HELPLINE Mahanthi S Raj1, Yashashwini Reddy M S2, Nandini Baitipuli3, Madhu4, Bindushree5 1,2,3,4 School of CSE REVA University Bangalore, India, 5Assistant professor, REVA University, Bangalore, India.
--------------------------------------------------------------------------***--------------------------------------------------------------------II LITERATURE REVIEW Abstract— The project develops machine learning-based strategies for precise gather yield statistics. The project makes the assumption that the rapid developments in machine learning (ML) and distinguishing calculation will provide practical and comprehensive solutions for improved harvest and environmental condition assessment. As we undoubtedly already know, India has the world's secondlargest population, and the majority of its citizens work in the horticulture industry. Farmers repeatedly produce the same harvests without trying new varieties of yields, and they apply manures in irregular amounts without realizing how much is missing in both substance and quantity. Thus, this directly affects agricultural yield in addition to causing the soil to ferment and harming the top layer. In this way, we developed the foundation for farmers' advancement using AI calculations.
1. Crop Prediction using Machine Learning Approaches, Nischitha K, Dhanush Vishwakarma, Mahendra N, Ashwini, Manjuraju M.R,2022 As we are undoubtedly aware, India is the world's second most populous country, with agribusiness being the most common occupation for the majority of Indians. Farmers continue to develop the same harvests without trying new varieties of yields, and they apply composts in arbitrary amounts without understanding the lack of substance and amount. As a result, this directly affects crop output while also causing dirt fermentation and harming the top layer. As a result, we designed the structure for rancher development using AI calculations. Our framework will offer the optimum suited yield for specific land in the context of its makeup and natural requirements. The framework also provides information on the necessary quantity and type of manure, in addition to the essential seeds for growth. Due to the way we're set up, farmers may produce a wider range of harvest, increase net income, and avoid soil contamination.
Keywords - Crop recommendation, Machine learning algorithms, Accuracy.
I. Introduction I. One of the important occupations practiced in India is farming. It is the largest banking sector and plays a major role in the advancement of the nation as a whole. To address the problems facing 1.3 billion individuals worldwide, more than 60% of the country's territory is used for horticulture adopting new agribusiness tools after that. Based on Farmers' experience in a particular region, previous crop and yield expectations were made. The ongoing situation without a change in the harvest and the application of insufficient amounts of supplements to the soil causes a decrease in the output, soil contamination (soil fermentation), and damage to the top layer.
2.Enhancing Crop Yield Prediction Utilizing Machine Learning on SatelliteBased Vegetation Health Indices Hoa Thi Pham,Joseph Awange , Michael Kuhn , Binh Van Nguyen , Luyen K Bui,2022 Exact gather result determination is fundamental in the distinctive design of the food sector, where estimates from the agricultural condition document (VCI), the warm situation record (TCI), and the simulated intelligence (ML) are combined. The drawback is that a one-size-fits-all assumption framework is typically applied throughout a region as a whole, ignoring the spatial variance in subterritorial VCI and TCI brought on by environmental and weather conditions. Rehashed VCI/TCI data poses extra difficulties that have a detrimental effect on the models' predictions when nonlinear ML is used. To deal with the two upgrades, this study proposes a framework that (I) applies higher-demand spatial free part assessment and (ii) employs a mixture of key part assessment (PCA) and ML (i.e., PCA-ML blend) (i.e., PCA-ML blend). The suggested technique, like Vietnam, divides typical VCI/TCI spatial capriciousness into distinct sub districts. Instead of a onesize-fits-all methodology, sub-local rice yield evaluation
II. In order to create new possibilities, machine learning, a component of computerized reasoning, has emerged along with big data advancements and improved execution registering. The proposed framework will make the best yield recommendation for a given plot of property. In light of the soil's composition and factors affecting the environment, such as temperature, stickiness, and pH.
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