Technical analysis of crop production prediction using Machine Learning and Deep Learning Algorithms

Gehlot, Anita; Sidana, Neeru; Jawale, Deepali; Jain, Neetu; Singh, Bhaskar Pratap; Singh, Barjinder

doi:10.1109/icses55317.2022.9914206

Cited by 6 publications

(1 citation statement)

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“…IoT sensors can be utilized to monitor soil moisture, temperature, and other environmental aspects [4] the gathered data from the IoT sensors can be used to define the best time to plant, water, and harvest reaps. By using IoT sensors, farmers can guarantee that the reaps get the right amount of water and nutrients, which can improve their quality and yield [5,6]. Figure 1 illustrates IoT and machine learning based crop analysis and prediction process.…”

Section: Introductionmentioning

confidence: 99%

Crop Prediction Model using Machine Learning Algorithms

Elbaşi¹,

Zaki²,

Topcu³

et al. 2023

Preprint

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This research investigates the potential benefits of integrating machine learning algorithms and IoT sensors in modern agriculture. The focus is on optimizing crop production and reducing waste through informed decisions about planting, watering, and harvesting crops. The paper discusses the current state of machine learning and IoT in agriculture, highlighting key challenges and opportunities. It also presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, including real-time data from IoT sensors, farmers can make more informed verdicts about factors that affect crop growth. Eventually, the integration of these technologies can transform modern agriculture by increasing crop yields while minimizing waste. In our studies, we achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99. 46% using Naïve Bayes Classifier, and Hoeffding Tree algorithms. Our results indicate that we achieved high accuracy results in our experiments in order to increase crop growth.

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Section: Introductionmentioning

confidence: 99%