Real-Time Detection of Water Stress in Corn Using Image Processing and Deep Learning

2020 ◽  
Author(s):  
Mor Soffer ◽  
Naftali Lazarovitch ◽  
Ofer Hadar
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Water Stress ◽  
Crop Production ◽  
Irrigation Treatment ◽  
Agricultural Crop ◽  
Learning Techniques ◽  
Web Camera ◽  
Use Efficiency ◽  
Non Destructive

<p>Water limitation is one of the main environmental constraints that adversely affects agricultural crop production around the world. Precise and rapid detection of plant water stress is critical for increasing agricultural productivity and water use efficiency. Numerous studies conducted over the years have attempted to find effective ways to correctly recognize situations of water stress in order to determine irrigation regimes.</p><p>Water stress detection is currently done by various methods that are not ideal; these methods are often very expensive, destructive and cumbersome. Water stress in plants is also expressed at different visual levels. Image processing is alternative way to visually recognize water stress levels. Such analysis is non-destructive, inexpensive and allows to examine the spatial variability of stress level under field conditions.</p><p>In our study, we propose a new method for detecting water stress in corn using image processing and deep learning. For the purpose of collecting the images, we performed a three-months experiment, in which we took images of five different groups of corn. Each group had a different irrigation treatment, which led to five different levels of water stress. The images were collected using a web camera located approximately 2 m from the plants.</p><p>Stress classification was done by inserting processed images into a Convolutional Neural Network (CNN). Training the network was done using transfer-learning techniques in order to exploit the performance of an already trained CNN, for a fast and efficient training over the dataset. Testing the quality of classification was done using extra camera which took a different set of images.</p><p>Results were tested upon two sub-experiments - classification of three types of treatments and classification of five types of treatments; the results were 98% accuracy in classification into three types of treatments (well-watered, reduced-watered and draught stressed treatment), and 85% accuracy in classification into five different treatments. These initial results are definitely excellent and can certainly serve decision making for optimal irrigation. <strong> </strong></p>

scholarly journals Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1551
Author(s):  
Tamoor Khan ◽  
Jiangtao Qiu ◽  
Hafiz Husnain Raza Sherazi ◽  
Mubashir Ali ◽  
Sukumar Letchmunan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Crop Production ◽  
Agricultural Development ◽  
Production Efficiency ◽  
Fruit Production ◽  
National Bureau ◽  
System A ◽  
Future Production ◽  
Learning Techniques ◽  
Agricultural Yields

Agricultural advancements have significantly impacted people’s lives and their surroundings in recent years. The insufficient knowledge of the whole agricultural production system and conventional ways of irrigation have limited agricultural yields in the past. The remote sensing innovations recently implemented in agriculture have dramatically revolutionized production efficiency by offering unparalleled opportunities for convenient, versatile, and quick collection of land images to collect critical details on the crop’s conditions. These innovations have enabled automated data collection, simulation, and interpretation based on crop analytics facilitated by deep learning techniques. This paper aims to reveal the transformative patterns of old Chinese agrarian development and fruit production by focusing on the major crop production (from 1980 to 2050) taking into account various forms of data from fruit production (e.g., apples, bananas, citrus fruits, pears, and grapes). In this study, we used production data for different fruits grown in China to predict the future production of these fruits. The study employs deep neural networks to project future fruit production based on the statistics issued by China’s National Bureau of Statistics on the total fruit growth output for this period. The proposed method exhibits encouraging results with an accuracy of 95.56% calculating by accuracy formula based on fruit production variation. Authors further provide recommendations on the AGR-DL (agricultural deep learning) method being helpful for developing countries. The results suggest that the agricultural development in China is acceptable but demands more improvement and government needs to prioritize expanding the fruit production by establishing new strategies for cultivators to boost their performance.

Multi-Mineral Segmentation of SEM Images Using Deep Learning Techniques

2021 ◽  
Author(s):  
Vladislav Vasilevich Alekseev ◽  
Denis Mihaylovich Orlov ◽  
Dmitry Anatolevich Koroteev
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Pore Space ◽  
Rock Physics ◽  
Sem Images ◽  
Digital Rock Physics ◽  
Learning Techniques ◽  
Digital Core ◽  
Non Destructive

Abstract The approaches of building and methods of using the digital core are currently developing rapidly. The use of these methods makes it possible to obtain petrophysical information by non-destructive methods quickly. Digital rock physics includes two main stages: constructing models and modeling various physical processes on the obtained models. Our work proposes using deep learning methods for mineral and pore space segmentation instead of classical methods such as threshold image processing. Deep neural networks have long been able to show their advantages in many areas of computer vision. This paper proposes and tests methods that help identify different minerals in images from a scanning electron microscope. We used images of rocks of the Achimov formation, which are arkoses, as samples. We tested various deep neural networks such as LinkNet, U-Net, ResUNet, and pix2pix and identified those that performed best in segmentation.

scholarly journals The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

2019 ◽  
Vol 63 (11) ◽  
pp. 1658-1667
Author(s):  
M J Castro-Bleda ◽  
S España-Boquera ◽  
J Pastor-Pellicer ◽  
F Zamora-Martínez
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Supervised Learning ◽  
Image Enhancement ◽  
Super Resolution ◽  
Supervised Machine Learning ◽  
Text Documents ◽  
Learning Techniques ◽  
Printed Text

Abstract This paper presents the ‘NoisyOffice’ database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems.

scholarly journals Automatic Analysis of Bees’ Waggle Dance

2020 ◽  
Author(s):  
Jordan Reece ◽  
Margaret Couvillon ◽  
Christoph Grüter ◽  
Francis Ratnieks ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Background Subtraction ◽  
Time Frame ◽  
Waggle Dance ◽  
Automatic Analysis ◽  
Learning Techniques ◽  
Low Pass ◽  
Processing Steps ◽  
Traditional Image

AbstractThis work describe an algorithm for the automatic analysis of the waggle dance of honeybees. The algorithm analyses a video of a beehive with 13,624 frames, acquired at 25 frames/second. The algorithm employs the following traditional image processing steps: conversion to grayscale, low pass filtering, background subtraction, thresholding, tracking and clustering to detect run of bees that perform waggle dances. The algorithm detected 44,530 waggle events, i.e. one bee waggling in one time frame, which were then clustered into 511 waggle runs. Most of these were concentrated in one section of the hive. The accuracy of the tracking was 90% and a series of metrics like intra-dance variation in angle and duration were found to be consistent with literature. Whilst this algorithm was tested on a single video, the ideas and steps, which are simple as compared with Machine and Deep Learning techniques, should be attractive for researchers in this field who are not specialists in more complex techniques.

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