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.

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>

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

Impact of Nitrogen Management Practices on Total Nitrogen in the Fruits of High Productive Hamlin Orange Trees

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 498e-498
Author(s):  
S. Paramasivam ◽  
A.K. Alva
Keyword(s):  
Crop Production ◽  
Management Practices ◽  
Fine Sand ◽  
Fruit Production ◽  
Perennial Crop ◽  
Total N ◽  
N Removal ◽  
Leaf N Concentration ◽  
N Rates ◽  
Orange Trees

For perennial crop production conditions, major portion of nutrient removal from the soil-tree system is that in harvested fruits. Nitrogen in the fruits was calculated for 22-year-old `Hamlin' orange (Citrus sinensis) trees on Cleopatra mandarin (Citrus reticulata) rootstock, grown in a Tavares fine sand (hyperthermic, uncoated, Typic Quartzipsamments) that received various N rates (112, 168, 224, and 280 kg N/ha per year) as either i) broadcast of dry granular form (DGF; four applications/year), or ii) fertigation (FRT; 15 applications/year). Total N in the fruits (mean across 4 years) varied from 82 to 110 and 89 to 111 kg N/ha per year for the DGF and FRT sources, respectively. Proportion of N in the fruits in relation to N applied decreased from 74% to 39% for the DGF and from 80% to 40% for the FRT treatments. High percentage of N removal in the fruits in relation to total N applied at low N rates indicate that trees may be depleting the tree reserve for maintaining fruit production. This was evident, to some extent, by the low leaf N concentration at the low N treatments. Furthermore, canopy density was also lower in the low N trees compared to those that received higher N rates.

Deep Learning Techniques for Koala Activity Detection

2018 ◽  
Author(s):  
Ivan Himawan ◽  
Michael Towsey ◽  
Bradley Law ◽  
Paul Roe
Keyword(s):  
Deep Learning ◽  
Activity Detection ◽  
Learning Techniques
Sign in / Sign up

Export Citation Format

Share Document