Detecting Crop Maturity Stages from Field Images

DOI

10.2991/978-94-6239-697-5_19How to use a DOI?

Keywords

Crop maturity detection; Computer vision; deep learning; Convolutional neural networks (CNNs); image classification; maturity stages; color; texture; shape analysis; agricultural automation; harvest timing optimization; root mean square error (RMSE); mean absolute error (MAE)

Abstract

Detecting crop maturity plays a vital role in modern agriculture as it directly affects harvest timing, yield quality, and global food availability. However, traditional maturity assessment methods mainly rely on farmers’ manual observations, which are time-consuming and prone to human error. Proper and timely detection of crop maturity phases guarantees optimum harvesting time decision-making, hence reducing post-harvest losses and maximizing nutrition and marketability. All these limitations often produce misleading maturity estimates, causing premature or delayed harvest, which can drastically reduce both yield quantity and quality.

To counter these challenges, this study presents a computer vision-based, automated method for robust and effective crop maturity detection from field-collected images. Tapping into the potential of Deep Learning algorithms, that is, Convolutional Neural Networks (CNNs), the given framework has the ability to identify and interpret important visual indicators like color gradient, texture, and morphological forms to determine the accurate crop classification into defined maturity phases — that is, earlystage (green), middle-stage (yellowing), and mature stage (harvest ready). The CNN model is learned using a diverse and exhaustive dataset comprising high-resolution images of various crop varieties, including wheat, rice, and soybeans, under a broad spectrum of environmental and illumination conditions to provide model robustness and generalizability.

Experimental verification of the system proposed proved encouraging results, which provided an overall classification rate of 82% across diverse crop species and field environments. In addition, the system also demonstrated consistent performance with a Root Mean Square Error (RMSE) of 4.34 and a Mean Absolute Error (MAE) of 4.56, thus showcasing its accuracy and reliability even in adverse real-world agricultural conditions. The results of this study represent a leap forward in the automation of crop monitoring activities, providing an intelligent and scalable solution for farmers and agronomists to make better decisions and increase agricultural productivity.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Anukool Yadav
AU  - Aniket Pratap Singh
AU  - Ayush Pandey
AU  - Himanshu Garg
AU  - Jobanpreet Singh
PY  - 2026
DA  - 2026/06/04
TI  - Detecting Crop Maturity Stages from Field Images
BT  - Proceedings of the Conference on Bridging Engineering Disciplines with AI and Machine Learning (BEDAIML 2026)
PB  - Atlantis Press
SP  - 220
EP  - 232
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-697-5_19
DO  - 10.2991/978-94-6239-697-5_19
ID  - Yadav2026
ER  -