Authors: Dr. Sulochana Sonkamble, Punit Jadhav, Vaishnavi Jadhav, Akanksha Kavitake, Rohan Kohalli
DOI Link: https://doi.org/10.22214/ijraset.2023.53034
Certificate: View Certificate
India is a predominantly agricultural nation. Agriculture is currently the most significant emerging sector in the actual world and the key industry and economic pillar of our nation. The discipline of agricultural information technology has recently undergone significant changes that have made crop yield prediction an intriguing study topic. Crop yield prediction is a technique for estimating crop production using many characteristics, including temperature, rainfall, fertilisers, pesticides, and other climatic variables and parameters. The use of data mining tools is quite common in agriculture. Agriculture uses data mining tools to forecast agricultural production for next years and evaluates these strategies. This method provides a succinct study of K-Nearest Neighbour (KNN) and Support Vector Machine-based agricultural yield prediction.
I. INTRODUCTION
India is described by little homesteads. Over 75percent of complete land capitals inside the nation are under 5 sections of land. Most harvests are downpour fed, with pretty much 45percent of the land inundated. According to certain assessments, around 55percent of complete populace of India relies upon cultivating. In the US, in light of weighty automation of farming, it is around 5percent. India is one of the greatest makers of agrarian items regardless has extremely less ranch efficiency. Efficiency should be expanded so ranchers can get additional compensation from a similar land parcel with less work. Accuracy horticulture gives a method for getting it done.
Accuracy cultivating , as the name suggests, alludes to the applying of exact and legitimate all out of remark like pee , composts, soil and so on at the appropriate opportunity to the stomach for expanding its efficiency and expanding its yields. Not all accuracy horticulture frameworks offer best outcomes. Yet, in farming it is vital that the suggestions made are exact and exact in light of the fact that in the event of mistakes it might prompt weighty material and capital misfortune. Many explores are being completed, to accomplish a precise and proficient model for crop expectation. Ensembling is one such method that is remembered for such examination works. Among these different AI procedures that are being utilized in this field; this paper proposes .
II. LITERATURE SURVEY
Ashwani kumar Kushwaha, Depicts crop yield forecast techniques and a propose reasonable harvest with the goal that it will work on the benefit for the rancher and nature of the agribusiness area. In this paper for crop yield expectation they acquire huge volume information, it’s been called as large information (soil and climate information) utilizing Hadoop stage and agro calculation. Subsequently based store information will foresee the appropriateness crop for specific condition what’s more improvement crop quality. Girish L, portray the harvest yield and downpour fall expectation utilizing an AI strategy. In this paper they gone through an alternate AI approaches for the expectation of precipitation and harvest yield and furthermore notice the effectiveness of an alternate AI calculation like liner relapse, SVM, KNN strategy and choice tree. In that calculation they presume that SVM have the most elevated productivity for precipitation expectation. Rahul katarya ,Portrays the distinctive AI strategies utilized for speeding up crop yield. In this paper they gone through various man-made brainpower methods such as AI calculation, enormous information investigation for accuracy agribusiness. They clarify about crop recommender framework utilizing KNN, Ensemble-based Models, Neural organizations,and so forth.
The planned framework will suggest the most reasonable yield for specific land. In light of climate boundary and soil content like Rainfall, Temperature, Humidity and pH. They are gathered from V C Farm Mandya, Government site furthermore climate office. The framework takes the necessary info from the ranchers or sensors like Temperature, Humidity what’s more pH. This all sources of info information applies to AI prescient calculations like Support Vector Machine (SVM) also Decision tree to recognize the example among information andthen, at that point, process it according to include conditions.
III. SYSTEM ARCHITECTURE
IV. MODULAR EXPLANATION
V. MOTIVATION
Machine learning enables us to create precise soil maps by analyzing various soil parameters such as pH levels, organic matter content, nutrient composition, and texture. These maps help farmers understand the spatial distribution of soil properties within their fields, allowing them to tailor their agricultural practices accordingly. By optimizing fertilizer application, irrigation schedules, and seed selection based on specific soil conditions, farmers can enhance productivity while minimizing resource wastage.
VI. OBJECTIVE OF THE SYSTEM
VII. METHODOLOGY
IX. ADVANTAGES
X. LIMITATIONS
XI. APPLICATIONS
XII. FUTURE SCOPE
XIII. ACKNOWLEGMENT
We take this occasion to thank God, almighty for blessing me with his grace and taking our Endeavor to a successful Culmination. Sincere and heartfelt thanks to my esteemed guide, Dr. Sulochana Sonkamble and the industry people, for providing me with the right guidance and advice at the crucial junctures and for showing me the right way Above all, I thank the Almighty, the source of all knowledge, understanding and wisdom.
Agriculture has always been the most important sector for survival. There are a lot of difficulties faced by our farmers these days due to various unpredictable reasons. Hence, as engineers, we need to collaborate with farmers and provide them a solution to improve the quality and quantity of crops. Our project is the first step towards it. Prediction can help us make strategic decisions in crop production. With machine learning, we get insights about the crop life which can be very beneficial. This work can be enhancing to the next level.
[1] Mamunur Rashid , Yusri Yusup, “A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction”, IEEE , 2021 [2] Jaydeep Yadav and Shalu Chopra, “Soil Analysis and Crop Fertility Prediction using Machine. Learning”, International Journal of Innovative Research in Advanced Engineering, 202 [3] .M.Kishore,” Crop Prediction using Machine Learning”, IEEE 2020. [4] Mythresh A and Lavanya B, “Crop Prediction using Machine Learning”, International Research Journal of Engineering and Technology , 2020 [5] Arun Kumar, Naveen Kumar, Vishal Vats.”Efficient crop yield prediction using machine learning algorithms”, IJRET Volume: 05 Issue: 06, June-2018, pp 3151-3159 [6] Vaneesbeer Singh, Abid Sarwar, Vinod Sharma. “Analysis of soil and prediction of crop yield (Rice) using Machine Learning approach”, IJARCS 8 (5), May-June 2017, pp 1254-1259. [7] Omkar Buchade, Nilesh Mehra, Shubham Ghodekar, Chandan Mehta “Crop Prediction system using machine Learning”, International Journal of Advance Engineering and Research Development , 2017 [8] Y. Everingham, J. Sexton, D. Skocaj, and G. Inman-Bamber. “Accurate prediction of sugarcane yield using a random forest algorithm”, Agronomy for Sustainable Development, vol. 36, no. 2, 2016. [9] Umid Kumar Dey, Abdullah Hasan Masud, Mohammed Nazim Uddin, “Rice Yield Prediction Model Using Data Mining”, ECCE, IEEE 2017, pp 321-326. 10. Niketa Gandhi, Owaiz Petkar, Leisa J. Armstrong,” PredictingRice Crop Yield Using Bayesian Networks”, ICACCI,IEEE 2016,pp 795-799.
Copyright © 2023 Dr. Sulochana Sonkamble, Punit Jadhav, Vaishnavi Jadhav, Akanksha Kavitake, Rohan Kohalli. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET53034
Publish Date : 2023-05-26
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here