scholarly journals State-of-the-art on Precision Agriculture

2003 ◽  
Vol 12 (4) ◽  
pp. 259-273 ◽  
Author(s):  
Sakae Shibusawa
Keyword(s):  
Precision Agriculture ◽  
State Of The Art

scholarly journals Applications of UAV Thermal Imagery in Precision Agriculture: State of the Art and Future Research Outlook

2020 ◽  
Vol 12 (9) ◽  
pp. 1491 ◽  
Author(s):  
Gaetano Messina ◽  
Giuseppe Modica
Keyword(s):  
Precision Agriculture ◽  
State Of The Art ◽  
Plant Stress ◽  
Stress Conditions ◽  
Plant Diseases ◽  
Basic Knowledge ◽  
Plant Phenotyping ◽  
Future Research ◽  
Thermal Data ◽  
Low Altitude

Low-altitude remote sensing (RS) using unmanned aerial vehicles (UAVs) is a powerful tool in precision agriculture (PA). In that context, thermal RS has many potential uses. The surface temperature of plants changes rapidly under stress conditions, which makes thermal RS a useful tool for real-time detection of plant stress conditions. Current applications of UAV thermal RS include monitoring plant water stress, detecting plant diseases, assessing crop yield estimation, and plant phenotyping. However, the correct use and interpretation of thermal data are based on basic knowledge of the nature of thermal radiation. Therefore, aspects that are related to calibration and ground data collection, in which the use of reference panels is highly recommended, as well as data processing, must be carefully considered. This paper aims to review the state of the art of UAV thermal RS in agriculture, outlining an overview of the latest applications and providing a future research outlook.

scholarly journals CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1058 ◽  
Author(s):  
Yang-Yang Zheng ◽  
Jian-Lei Kong ◽  
Xue-Bo Jin ◽  
Xiao-Yi Wang ◽  
Min Zuo
Keyword(s):  
Deep Learning ◽  
Precision Agriculture ◽  
Production Efficiency ◽  
State Of The Art ◽  
Detection Accuracy ◽  
Similar Species ◽  
Learning Technology ◽  
Well Performance ◽  
Species Classification ◽  
Domain Specific

Intelligence has been considered as the major challenge in promoting economic potential and production efficiency of precision agriculture. In order to apply advanced deep-learning technology to complete various agricultural tasks in online and offline ways, a large number of crop vision datasets with domain-specific annotation are urgently needed. To encourage further progress in challenging realistic agricultural conditions, we present the CropDeep species classification and detection dataset, consisting of 31,147 images with over 49,000 annotated instances from 31 different classes. In contrast to existing vision datasets, images were collected with different cameras and equipment in greenhouses, captured in a wide variety of situations. It features visually similar species and periodic changes with more representative annotations, which have supported a stronger benchmark for deep-learning-based classification and detection. To further verify the application prospect, we provide extensive baseline experiments using state-of-the-art deep-learning classification and detection models. Results show that current deep-learning-based methods achieve well performance in classification accuracy over 99%. While current deep-learning methods achieve only 92% detection accuracy, illustrating the difficulty of the dataset and improvement room of state-of-the-art deep-learning models when applied to crops production and management. Specifically, we suggest that the YOLOv3 network has good potential application in agricultural detection tasks.

State‐of‐the‐art technologies in precision agriculture: a systematic review

2019 ◽  
Vol 99 (11) ◽  
pp. 4878-4888 ◽  
Author(s):  
Ishita Bhakta ◽  
Santanu Phadikar ◽  
Koushik Majumder
Keyword(s):  
Systematic Review ◽  
Precision Agriculture ◽  
State Of The Art

scholarly journals Pursuing Precision Horticulture with the Internet and a Spreadsheet

2000 ◽  
Vol 10 (3) ◽  
pp. 458-467 ◽  
Author(s):  
Timothy L. Righetti ◽  
Michael D. Halbleib
Keyword(s):  
Precision Agriculture ◽  
State Of The Art ◽  
Information Analysis ◽  
Process Data ◽  
Positioning Systems ◽  
Systems Software ◽  
Global Positioning ◽  
Technology Applications ◽  
Sales Industry ◽  
Analytical Tools

Agriculture is changing. State-of-the-art computer systems that use GPS (global positioning systems) data, GIS (geographic information systems) software, remotely sensed images, automated sampling, and information analysis systems are transforming growers' ability to produce their crops. Currently, the farm service and agricultural sales industry, rather than the grower direct most information technology applications. Precision agriculture must become an information-driven and grower-driven process. Data evaluation has to be made simpler, less time consuming, and inexpensive. The purpose of this paper is to outline potential strategies and demonstrate how information can be processed and evaluated with readily available and inexpensive analytical tools.

scholarly journals A Comparative Study of Semantic Segmentation Models for Identification of Grape with Different Varieties

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 997
Author(s):  
Yun Peng ◽  
Aichen Wang ◽  
Jizhan Liu ◽  
Muhammad Faheem
Keyword(s):  
Image Enhancement ◽  
Precision Agriculture ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Histogram Equalization ◽  
Performance Difference ◽  
Convolutional Network ◽  
Enhancement Method ◽  
The Impact ◽  
Segmentation Models

Accurate fruit segmentation in images is the prerequisite and key step for precision agriculture. In this article, aiming at the segmentation of grape cluster with different varieties, 3 state-of-the-art semantic segmentation networks, i.e., Fully Convolutional Network (FCN), U-Net, and DeepLabv3+ applied on six different datasets were studied. We investigated: (1) the segmentation performance difference of the 3 studied networks; (2) The impact of different input representations on segmentation performance; (3) The effect of image enhancement method to improve the poor illumination of images and further improve the segmentation performance; (4) The impact of the distance between grape clusters and camera on segmentation performance. The experiment results show that compared with FCN and U-Net the DeepLabv3+ combined with transfer learning is more suitable for the task with an intersection over union (IoU) of 84.26%. Five different input representations, namely RGB, HSV, L*a*b, HHH, and YCrCb obtained different IoU, ranging from 81.5% to 88.44%. Among them, the L*a*b got the highest IoU. Besides, the adopted Histogram Equalization (HE) image enhancement method could improve the model’s robustness against poor illumination conditions. Through the HE preprocessing, the IoU of the enhanced dataset increased by 3.88%, from 84.26% to 88.14%. The distance between the target and camera also affects the segmentation performance, no matter in which dataset, the closer the distance, the better the segmentation performance was. In a word, the conclusion of this research provides some meaningful suggestions for the study of grape or other fruit segmentation.

scholarly journals Precision Agriculture Technology: A Literature Review

2020 ◽  
pp. 30-34
Author(s):  
C. A. Danbaki ◽  
N. C. Onyemachi ◽  
D. S. M. Gado ◽  
G. S. Mohammed ◽  
D. Agbenu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Precision Agriculture ◽  
State Of The Art ◽  
Irrigation Management ◽  
Conventional Farming ◽  
Livestock Farming ◽  
Current State ◽  
Art Methods ◽  
Increase In Productivity

This study is a survey on state-of-the-art methods based on artificial intelligence and image processing for precision agriculture on Crop Management, Pest and Disease Management, Soil and Irrigation Management, Livestock Farming and the challenges it presents. Precision agriculture (PA) described as applying current technologies into conventional farming methods. These methods have proved to be highly efficient, sustainable and profitable to the farmer hence boosting the economy. This study is a survey on the current state of the art methods applied to precision agriculture. The application of precision agriculture is expected to yield an increase in productivity which ultimately ends in profit to the farmer, to the society increase sustainability and also improve the economy.

scholarly journals Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey

2020 ◽  
Author(s):  
Vineeth N Balasubramanian ◽  
Wei Guo ◽  
Akshay L Chandra ◽  
Sai Vikas Desai
Keyword(s):  
Deep Learning ◽  
Precision Agriculture ◽  
State Of The Art ◽  
Crop Management ◽  
Plant Phenotyping ◽  
Crop Monitoring ◽  
The World ◽  
Management Techniques ◽  
Art Research ◽  
Increase Crop Yield

In light of growing challenges in agriculture with ever growing food demand across the world, efficient crop management techniques are necessary to increase crop yield. Precision agriculture techniques allow the stakeholders to make effective and customized crop management decisions based on data gathered from monitoring crop environments. Plant phenotyping techniques play a major role in accurate crop monitoring. Advancements in deep learning have made previously difficult phenotyping tasks possible. This survey aims to introduce the reader to the state of the art research in deep plant phenotyping.

scholarly journals Detecting Citrus in Orchard Environment by Using Improved YOLOv4

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Wenkang Chen ◽  
Shenglian Lu ◽  
Binghao Liu ◽  
Guo Li ◽  
Tingting Qian
Keyword(s):  
Precision Agriculture ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Fine Tuning ◽  
Natural Environments ◽  
Average Increase ◽  
Recognition Method ◽  
Network Layers ◽  
Rgb Images ◽  
Increase In Accuracy

Real-time detection of fruits in orchard environments is one of crucial techniques for many precision agriculture applications, including yield estimation and automatic harvesting. Due to the complex conditions, such as different growth periods and occlusion among leaves and fruits, detecting fruits in natural environments is a considerable challenge. A rapid citrus recognition method by improving the state-of-the-art You Only Look Once version 4 (YOLOv4) detector is proposed in this paper. Kinect V2 camera was used to collect RGB images of citrus trees. The Canopy algorithm and the K-Means++ algorithm were then used to automatically select the number and size of the prior frames from these RGB images. An improved YOLOv4 network structure was proposed to better detect smaller citrus under complex backgrounds. Finally, the trained network model was used for sparse training, pruning unimportant channels or network layers in the network, and fine-tuning the parameters of the pruned model to restore some of the recognition accuracy. The experimental results show that the improved YOLOv4 detector works well for detecting different growth periods of citrus in a natural environment, with an average increase in accuracy of 3.15% (from 92.89% to 96.04%). This result is superior to the original YOLOv4, YOLOv3, and Faster R-CNN. The average detection time of this model is 0.06 s per frame at 1920 × 1080 resolution. The proposed method is suitable for the rapid detection of the type and location of citrus in natural environments and can be applied to the application of citrus picking and yield evaluation in actual orchards.

scholarly journals Big Data Challenges and Opportunities in Agriculture

2020 ◽  
Vol 11 (1) ◽  
pp. 48-66
Author(s):  
Maya Gopal P.S. ◽  
Bhargavi Renta Chintala
Keyword(s):  
Big Data ◽  
Precision Agriculture ◽  
State Of The Art ◽  
Complete Picture ◽  
Data Generation ◽  
Comprehensive Overview ◽  
Future Directions ◽  
Challenges And Opportunities ◽  
Technical Challenges ◽  
Exciting Area

This article reviews various aspects of research concerning the background and state-of-the-art of big data in agriculture. This article focuses on data generation, storage, analysis and visualization in big data. In every phase, technical challenges and the latest advancement are discussed, and these discussions aim to provide a comprehensive overview and complete picture of this exciting area. This survey is concluded with a discussion on the application of big data in precision agriculture and its future directions.

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