Soil Analysis and Crop Recommendation using Deep Learning
DOI:
https://doi.org/10.15680/IJCTECE.2019.0202004Keywords:
Soil analysis, Crop recommendation, Deep learning, Precision agriculture, Smart farming, Artificial intelligence (AI), Machine learning (ML), Convolutional neural networks (CNN)Abstract
Crop prediction is a task that involves using deep learning algorithms to predict crop yields and other relevant metrics based on a variety of factors, such as weather conditions, soil data, and historical crop data. The goal of this task is to provide farmers and other stakeholders with accurate and reliable information about expected crop yields, which can help them to make better decisions about planting, harvesting, and other aspects of agricultural management. The problem of crop prediction involves several challenges, including the need for accurate and timely data, the selection of relevant features and parameters for analysis, and the development of suitable machine learning models for prediction. Moreover, the prediction accuracy may also be affected by factors such as regional variations in climate and soil conditions, as well as the presence of pests and other environmental factors. To address these challenges, researchers and developers in the field of crop prediction have developed a variety of techniques, including data pre-processing, feature selection, deep learning model selection, and performance evaluation. These techniques may involve the use of different types of data, such as weather data, soil data, and crop data, as well as various deep learning algorithms, such as multi-layer perceptron algorithm and Convolutional neural network algorithm. Ultimately, the success of crop prediction depends on the ability of the system to accurately and reliably analyse data from a variety of sources, and then predict crop yields and other relevant metrics with a high degree of accuracy. By addressing these challenges, crop prediction has the potential to improve agricultural productivity and sustainability, and to support the development of more efficient and effective farming practices
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