AGRISMART: Crop and Soil Management System

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]26/09/2025 at 11:11 AM[/if 2224] | [if 1553 equals=””] Volume : 16 [else] Volume : 16[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 50 55

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    B.A. Khivsara, Jain Sakshi Vijay, Shewale Komal Sharad, Kothawade Pranjal Sanjay, Chhajed Palak Narendra,

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  1. Assistant Professor, Student, Student, Student, Student, Department of Computer Engineering, Shri Neminath Jain Bramhacharyashram’s Late Sau Kantabai Bhavarlalji Jain College of Engineering, Chandwad, Department of Computer Engineering, Shri Neminath Jain Bramhacharyashram’s Late Sau Kantabai Bhavarlalji Jain College of Engineering, Chandwad, Department of Computer Engineering, Shri Neminath Jain Bramhacharyashram’s Late Sau Kantabai Bhavarlalji Jain College of Engineering, Chandwad, Department of Computer Engineering, Shri Neminath Jain Bramhacharyashram’s Late Sau Kantabai Bhavarlalji Jain College of Engineering, Chandwad, Department of Computer Engineering, Shri Neminath Jain Bramhacharyashram’s Late Sau Kantabai Bhavarlalji Jain College of Engineering, Chandwad, Maharashtra, Maharashtra, Maharashtra, Maharashtra, Maharashtra, India, India, India, India, India

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Abstract

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nAgriculture has played a crucial role in developing countries where the majority of the rural population relies on it for their livelihoods. A finer-grade crop classification has become crucial in the context of precision agriculture. In recent years, the volume of open image data has grown significantly. This can be used in combination with machine learning techniques to classify crop types in the agricultural industry. The proposed crop species recognition system is divided into three major parts. We use image pre-processing then cut down the crop feature images, and use classification using CNN. The first step is to improve the quality of input images and prepare them for analysis. Preprocessing typically includes adjusting the brightness and contrast, noise reduction, resizing, and normalization. This step is essential to standardize images, making them more compatible for feature extraction and classification. Next step after preprocessing is Feature extraction, in which relevant features of crop images are isolated. In this step, the model identifies unique characteristics like color, texture, and shape that help in distinguishing one crop type from another. This segmentation can involve techniques like edge detection, thresholding, or region-based segmentation, which can cut down unnecessary data and isolate crop-relevant information. Then comes the classification using convolutional neural networks. CNNs are particularly effective for image-based classification due to their ability to learn complex patterns. Here, the CNN model is trained with labeled images of various crop types. Once trained, the model can analyze new images and classify them according to learned patterns. CNN layers (convolutional, pooling, and fully connected) are designed to pick up on different aspects of the image, with deeper layers recognizing more complex structures in this system.nn

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Keywords: CNN, machine learning, preprocessing, classification, smart agriculture, soil and crop management system

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Remote Sensing & GIS ]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Remote Sensing & GIS (jorsg)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nB.A. Khivsara, Jain Sakshi Vijay, Shewale Komal Sharad, Kothawade Pranjal Sanjay, Chhajed Palak Narendra. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]AGRISMART: Crop and Soil Management System[/if 2584]. Journal of Remote Sensing & GIS. 10/09/2025; 16(03):50-55.

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How to cite this URL:
nB.A. Khivsara, Jain Sakshi Vijay, Shewale Komal Sharad, Kothawade Pranjal Sanjay, Chhajed Palak Narendra. [if 2584 equals=”][226 striphtml=1][else]AGRISMART: Crop and Soil Management System[/if 2584]. Journal of Remote Sensing & GIS. 10/09/2025; 16(03):50-55. Available from: https://journals.stmjournals.com/jorsg/article=10/09/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

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Journal of Remote Sensing & GIS

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[if 344 not_equal=””]ISSN: 2230-7990[/if 344]

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Volume 16
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 12/06/2025
Accepted 02/08/2025
Published 10/09/2025
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Publication Time 90 Days

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