Machine Learning in Soil Borne Diseases, Soil Data Analysis &amp; Crop Yielding: A Review

Kiruthiga, C.; Dharmarajan, K.

doi:10.1109/iitcee57236.2023.10091016

Cited by 6 publications

(1 citation statement)

References 25 publications

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“…In recent years, technologies such as artificial intelligence (AI) [1][2][3], the Internet of things (IoT) [4,5], electronics [6][7][8], and edge computing [9] have become essential for enhancing energy efficiency, autonomy, and sustainability in global agriculture systems. This is especially critical due to the challenges posed by exponential population growth [10], which affects food security and leads to complex sociocultural problems and precarious working conditions in fields in Spain [11].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Vision Techniques in Low-Cost Devices to Improve Efficiency in Precision Farming

Jaramillo-Hernández,

Julian,

Marco-Detchart

et al. 2024

Sensors

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In the context of recent technological advancements driven by distributed work and open-source resources, computer vision stands out as an innovative force, transforming how machines interact with and comprehend the visual world around us. This work conceives, designs, implements, and operates a computer vision and artificial intelligence method for object detection with integrated depth estimation. With applications ranging from autonomous fruit-harvesting systems to phenotyping tasks, the proposed Depth Object Detector (DOD) is trained and evaluated using the Microsoft Common Objects in Context dataset and the MinneApple dataset for object and fruit detection, respectively. The DOD is benchmarked against current state-of-the-art models. The results demonstrate the proposed method’s efficiency for operation on embedded systems, with a favorable balance between accuracy and speed, making it well suited for real-time applications on edge devices in the context of the Internet of things.

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