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Computer vision technology in agricultural automation —A review

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TLDR
It is found that the existing technology can help the development of agricultural automation for small field farming to achieve the advantages of low cost, high efficiency and high precision, but there are still major challenges.
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This article is published in Information Processing in Agriculture.The article was published on 2020-03-01 and is currently open access. It has received 228 citations till now.

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Incremental Machine Learning for Soft Pneumatic Actuators with Symmetrical Chambers

TL;DR: In this article , a modulo-free intelligent control of soft pneumatic actuators based on an incremental learning algorithm is proposed, where real-time data flow training is combined with incremental learning, and a neural network model is tuned sequentially for each data input.

Automated systems as a factor affecting on flowers plant harvesting

TL;DR: In this article , the authors investigated different fully automated harvesting systems for one of the most important crops, medicinal and aromatic plants, especially flowers which are involved in many industries and characterized by high economic importance in the global trade balance of crops.
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A smart aeroponic system for sustainable indoor farming

TL;DR: In this article , a methodology for developing a smart aeroponic system, based on IoT and artificial intelligence algorithms, is presented, which can automatically balance resource utilization (e.g., water, nutrients, energy) and crop productivity.
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Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications

TL;DR: A comprehensive review of the methods and applications related to crop row detection for agricultural machinery navigation is presented in this article , where the advantages and disadvantages of current mainstream crop-row detection methods, including various traditional methods and deep learning frameworks, are also discussed and summarized.
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Development of an electronic profilometer to measure mobilization variables in soil harrowing

TL;DR: In this paper , the authors developed an automatic data acquisition system for profilometry, evaluating four harrowing speeds and found that the increase in the mechanized set speed provided the reduction of the elevated area and soil blistering caused by the rise in disc rotation and consequent deviation of the soil particles.
References
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Journal ArticleDOI

Machine Learning in Agriculture: A Review.

TL;DR: A comprehensive review of research dedicated to applications of machine learning in agricultural production systems is presented, demonstrating how agriculture will benefit from machine learning technologies.
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Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

TL;DR: A survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry—wherein the combination of UAV and hyperspectrals plays a center role—is presented in this paper.
Journal ArticleDOI

Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

TL;DR: This work presents a systematic review that aims to identify the applicability of computer vision in precision agriculture for the production of the five most produced grains in the world: maize, rice, wheat, soybean, and barley.
Journal ArticleDOI

Modern Trends in Hyperspectral Image Analysis: A Review

TL;DR: This review focuses on the fundamentals of hyperspectral image analysis and its modern applications such as food quality and safety assessment, medical diagnosis and image guided surgery, forensic document examination, defense and homeland security, remote sensing applicationssuch as precision agriculture and water resource management and material identification and mapping of artworks.
Journal ArticleDOI

Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning.

TL;DR: The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set.
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