Machine Vision-Based Approaches for Predicting Tomato Yields
DOI:
https://doi.org/10.17108/ActAgrOvar.2024.65.1.89Keywords:
yield prediction, big data, machine learning, tomato detection, imagingAbstract
In this study, we delve into advanced technologies and sensors utilized for precision agriculture, especially in greenhouse environments. Our investigation encompasses information technology, statistical models, and neural network-based approaches for predicting and estimating crop yields, primarily through direct data analysis.
We highlight a significant limitation of current methods: they often do not cover the entire lifecycle of plants, which is critical due to the varying stages of plant development. Although these methods are widely used, their effectiveness can be constrained during the dynamic growth phases of plants, and they are adopted in the absence of better alternatives.
At this point, we introduce machine vision as a versatile tool with applications across numerous fields. Its key advantage lies in its ability to detect changes throughout the plant lifecycle, allowing us to segment the lifecycle into more manageable phases. This segmentation enables the targeted application of statistical, regression, and neural network techniques, with each system focusing on a specific developmental stage.
Machine vision is adept at extracting crucial information at different stages of a plant's life. It can be used for various purposes, such as weed monitoring, tracking plant growth, assessing leaf and stem health, detecting stress and early signs of disease, and evaluating flowering, crop progression, as well as determining maturity, quality, and yield.
To demonstrate the effectiveness of machine vision, we developed a Python application that identifies ripe tomatoes ready for harvest in RGB images. This tool aids in accurately estimating harvest volumes by counting the number of mature tomatoes.
Furthermore, we suggest the implementation of a multi-camera system employing machine vision to identify precise agricultural interventions needed at various stages of crop development.
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