[{“box”:0,”content”:”[if 992 equals=”Open Access”]n
n
Open Access
nn
n
n[/if 992]n
n
n
n
n
n
Parutagouda S Khanagoudar, Sushma B S, Chandana C N, Manikanta N, Spoorti Suresh Awatimath,
n
- n t
n
n
n[/foreach]
n
n[if 2099 not_equal=”Yes”]n
- [foreach 286] [if 1175 not_equal=””]n t
- Professor,, Assistant Professor, Student, Student, Student Department of Data Science and Engineering, Nagarjuna College of Engineering and Technology, Department of Data Science and Engineering, Nagarjuna College of Engineering and Technology,, Department of Data Science and Engineering, Nagarjuna College of Engineering and Technology, Department of Data Science and Engineering, Nagarjuna College of Engineering and Technology, Department of Data Science and Engineering, Nagarjuna College of Engineering and Technology Bangalore, Bangalore, Bangalore, Bangalore, Bangalore India, India, India, India
n[/if 1175][/foreach]
n[/if 2099][if 2099 equals=”Yes”][/if 2099]n
Abstract
nIndia’s economy is based primarily on agriculture, as over 50% of the country’s population depends on it for their livelihood. The long-term viability of agriculture is seriously threatened by variations in the weather, climate, and other environmental factors. Because machine learning provides tools for decision assistance in agricultural yield prediction, including guidance on which crops to plant and when to plant them during the growing season, it is essential to the process. I’ll see to it. The goal of this work is to extract and synthesise features that are used to predict agricultural productivity through a systematic review. Furthermore, a number of techniques have been created to examine crop yield prediction through the use of machine learning algorithms. The decline in crop yield forecast efficiency and the decrease in relative error are neural networks’ primary drawbacks. Similarly, picking fruit for sorting and classification proved difficult due to supervised learning methods’ inability to capture the non-linear connection between input and output variables. In order to produce precise and effective models for crop classification, several studies have been suggested for the advancement of agriculture. This research includes estimating crop yields based on weather, plant diseases, crop classification based on growth stage, and more. This paper investigates many machine learning (ML) approaches used to agricultural production prediction and offers a thorough evaluation of the approaches’ precision
n
Keywords: Decision Tree Regressor, KNN Regressor, Random Forest Regressor, Linear Regressorc
n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Cheminformatics(ijci)]
n
n
n
n
n
nn[if 992 equals=”Open Access”] Full Text PDF Download[/if 992] n
nn[if 379 not_equal=””]n
Browse Figures
n
n[/if 379]n
References
n[if 1104 equals=””]n
- M Alagurajan, C Vijayakumaran, 2020. ML Methods for Crop Yield Prediction and Estimation: An Exploration, International Journal of Engineering and Advanced Technology. 9(3):2249-8958.
- Jasmin Bharadiya, Nikolaos Tzenios, Manjunath Reddy, 2023. Forecasting of Crop Yield using Remote Sensing Data, Agrarian Factors and Machine Learning Approaches, Article in Journal of Engineering Research and Reports, 24(12):29-44.
- Potnuru Sai Nishant, Pinapa Sai Venkat, Bollu Lakshmi Avinash, B. jabber, 2020. Crop Yield Prediction based on Indian Agriculture using Machine Learning, International Conference for Emerging Technology (INCET), pp.1-4, doi: 10.1109/INCET49848.2020.9154036.
- Ersin Elbasi, Chamseddine Zaki, Ahmet E. Topcu, Wiem Abdelbaki, 2023. Crop Prediction Model Using Machine Learning Algorithms, Article in Advances in Technology Applied in Agricultural Engineering, Appl.Sci.2023, 13(16),9288.
- Dhivya Elavarasan, P. M. Durairaj Vincent, 2020. “Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications”, Article in IEEE Access, vol. 8, pp. 86886-86901, doi:10.1109/ACCESS.2020.2992480.
- Srinivasan S, et al. Deep convolutional neural network-based image spam classification. In 2020 6th conference on data science and machine learning applications (CDMA). IEEE; 2020.
- Archontoulis S V, et al. Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt. Crop Science. 2020; 60(2):721-738.
- Ersoz ES, Martin NF, Stapleton AE. On to the next chapter for crop breeding: convergence with data science. Crop Science. 2020;60(2):639-655.
- Washburn JD, Burch MB, Franco JAV. Predictive breeding for maize: Making use of molecular phenotypes, machine learning, and physiological crop models. Crop Science. 2020;60(2):622-638.
- Sarker IH. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Computer Sci. 2021;2(6):420.
nn[/if 1104][if 1104 not_equal=””]n
- [foreach 1102]n t
- [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]
n[/foreach]
n[/if 1104]
nn
nn[if 1114 equals=”Yes”]n
n[/if 1114]
n
n
n
| Volume | 01 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | May 10, 2024 | |
| Accepted | May 14, 2024 | |
| Published | July 20, 2024 |
n
n
n
n
n
n nfunction myFunction2() {nvar x = document.getElementById(“browsefigure”);nif (x.style.display === “block”) {nx.style.display = “none”;n}nelse { x.style.display = “Block”; }n}ndocument.querySelector(“.prevBtn”).addEventListener(“click”, () => {nchangeSlides(-1);n});ndocument.querySelector(“.nextBtn”).addEventListener(“click”, () => {nchangeSlides(1);n});nvar slideIndex = 1;nshowSlides(slideIndex);nfunction changeSlides(n) {nshowSlides((slideIndex += n));n}nfunction currentSlide(n) {nshowSlides((slideIndex = n));n}nfunction showSlides(n) {nvar i;nvar slides = document.getElementsByClassName(“Slide”);nvar dots = document.getElementsByClassName(“Navdot”);nif (n > slides.length) { slideIndex = 1; }nif (n (item.style.display = “none”));nArray.from(dots).forEach(nitem => (item.className = item.className.replace(” selected”, “”))n);nslides[slideIndex – 1].style.display = “block”;ndots[slideIndex – 1].className += ” selected”;n}n”}]