Use of Machine Learning and IoT in Agriculture

Mehla, Anuj; Deora, Sukhvinder Singh

doi:10.1007/978-3-031-04524-0_16

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…Numerous ML algorithms have been utilized to construct agricultural yield prediction models, yet a definitive conclusion has yet to be drawn on the overall model. Our research addresses this issue by investigating multiple ML algorithms, including MLR, XGBoost, and Random Forest, References (14,15) ML methods.…”

Section: Methodsmentioning

confidence: 99%

Improving Crop Yield Prediction Models with Optimization-Based Feature Selection and Filtering Approaches

Mehla,

Deora,

Dalal

2024

IJST

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Objective:To analyze the impact of various factors on crop yield and provide insights for improving crop production in the region. Methods: This research employs feature selection algorithms, machine learning models, and feature extraction algorithm Principal Component Analysis (PCA) technique to identify the key factors affecting crop yield in India. Data from the Indian Meteorological, Statistical, and Agriculture Departments spanning five decades are analyzed to provide valuable insights to policymakers and farmers. This research analyzed 20 factors in determining their impact on crop yield in the Indian economy. Three feature selection algorithms were used to identify the essential factors: forward feature selection, backward feature selection, and recursive feature elimination. These three algorithms were used to select the most important factors from the Twenty selected factors, and then three ML models were used to estimate the accuracy of the feature selection algorithms: Random Forest, XGBoost, and Multiple Linear Regression. Principal Component Analysis (PCA) was used for the dimensionality reduction of the features. RMSE, MAPE, MAE, and R2 were used to measure the feature selection method's performance. Findings: Out of the three machine learning algorithms, the Random Forest algorithm with the forward feature selection algorithm provided the highest model accuracy of 98.415 percent. Moreover, compare the combination of three machine learning algorithms and different feature selection algorithms. Novelty: Our approach to predicting crop yield is based on a combination of Feature Selection, PCA, and Machine Learning algorithms. This proposed research utilizes Feature Selection algorithms to identify the most crucial features among 20 available options and then apply Machine Learning models to make accurate predictions based on these features.https://www.indjst.org/

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Section: Methodsmentioning

confidence: 99%

Improving Crop Yield Prediction Models with Optimization-Based Feature Selection and Filtering Approaches

Mehla,

Deora,

Dalal

2024

IJST

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“…There are several ML algorithms used for crop yield prediction, and the final choice depends on the data characteristics and research objectives [3]. These algorithms [4][5][6][7][8] help manage complex relationships between input variables such as soil properties, weather patterns, crop management practices, and yield outcomes, improving prediction accuracy, and facilitating informed agricultural practices [9].…”

Section: Introductionmentioning

confidence: 99%

A Bibliometric Analysis of Machine Learning Techniques for Predicting Crop Yield

Mehla,

Deora

2024

Advancements in Communication and Systems

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Accurate crop yield prediction is paramount in agriculture to improve decision-making and resource allocation. It is a key part of precision agriculture, a sustainable farming approach that uses data and technology to improve crop yields while reducing environmental impact. Machine learning techniques have been extensively researched to enhance the accuracy of yield prediction. Athorough comprehension of the available literature on this topic is imperative. This study aims to categorize research, map relevant literature, and evaluate the progress made in yield prediction with machine learning over the last 13 years using bibliometric techniques. It analyzed the Scopus database for articles using search keywords "Yield Prediction," "Yield Estimation," and "Yield Forecasting" with "Machine Learning." It located 1177 pertinent articles, confirming the significance of machine learning in yield prediction. The Biblioshiny package analyzes the aggregated Scopus database, identifying current trends, challenges, and future research directions. The insights gained from this study can advance agricultural practices and contribute to sustainability efforts. Outcome: The study highlights the crucial role of machine learning in predicting crop yield accurately by using the Scopus database for finding articles, and the biblioshiny package of R is used to identify the current trends, challenges, and future research directions, which has the potential to transform precision agriculture by increasing crop yield and minimizing environmental impact.

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Design of a Non-Back-Drivable Screw Jack Mechanism for the Hitch Lifting Arms of Electric-Powered Tractors

2022

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The agricultural sector is constantly evolving. The rise in the world’s population generates an increasingly growing demand for food, resulting in the need for the agroindustry to meet this demand. Tractors are the vehicles that have made a real difference in agriculture’s development throughout history, lowering costs in soil tillage and facilitating activities and operations for workers. This study aims to successfully design and build an autonomous, electric agricultural tractor that can autonomously perform recurring tasks in open-field and greenhouse applications. This project is fully part of the new industrial and agronomic revolution, known as Factory 4.0 and Agriculture 4.0. The predetermined functional requirements for the vehicle are its lightweight, accessible price, the easy availability of its spare parts, and its simple, ordinary maintenance. In this first study, the preliminary phases of sizing and conceptual design of the rover are reported before subsequently proceeding to the dynamical analysis. To optimize the design of the various versions of the automated vehicle, it is decided that a standard chassis would be built based on a robot operating inside a greenhouse on soft and flat terrains. The SimScape multi-body environment is used to model the kinematics of the non-back-drivable screw jack mechanism for the hitch-lifting arms. The control unit for the force exerted is designed and analyzed by means of an inverse dynamics simulation to evaluate the force and electric power consumed by the actuators. The results obtained from the analysis are essential for the final design of the autonomous electric tractor.

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Use of Machine Learning and IoT in Agriculture

Cited by 3 publications

References 38 publications

Improving Crop Yield Prediction Models with Optimization-Based Feature Selection and Filtering Approaches

Improving Crop Yield Prediction Models with Optimization-Based Feature Selection and Filtering Approaches

A Bibliometric Analysis of Machine Learning Techniques for Predicting Crop Yield

Design of a Non-Back-Drivable Screw Jack Mechanism for the Hitch Lifting Arms of Electric-Powered Tractors

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