Smart Agriculture Using UAV and Deep Learning

2022 ◽  
pp. 1-16
Author(s):  
Krishna Keshob Paul ◽  
Jishnu Dev Roy ◽  
Sourav Sarkar ◽  
Sena Kumar Barai ◽  
Abu Sufian ◽  
...  
Keyword(s):  
Deep Learning ◽  
Smart Agriculture

scholarly journals A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 81
Author(s):  
Jianbin Xiong ◽  
Dezheng Yu ◽  
Shuangyin Liu ◽  
Lei Shu ◽  
Xiaochan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Plant Species ◽  
Image Recognition ◽  
Recurrent Neural Network ◽  
Plant Diseases ◽  
Learning Methods ◽  
Smart Agriculture ◽  
Important Branch

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.

Deep Learning and Computer Vision in Smart Agriculture

2020 ◽  
pp. 66-88
Author(s):  
Shiv Kumar ◽  
Agrima Yadav ◽  
Deepak Kumar Sharma
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Life Cycle ◽  
World Population ◽  
Traditional Methods ◽  
Demand And Supply ◽  
The World ◽  
Increasing Demand ◽  
Smart Agriculture ◽  
The Many

The exponential growth in the world population has led to an ever-increasing demand for food supplies. This has led to the realization that conventional and traditional methods alone might not be able to keep up with this demand. Smart agriculture is being regarded as one of the few realistic ways that, together with the traditional methods, can be used to close the gap between the demand and supply. Smart agriculture integrates the use of different technologies to better monitor, operate, and analyze different activities involved in different phases of the agricultural life cycle. Smart agriculture happens to be one of the many disciplines where deep learning and computer vision are being realized to be of major impact. This chapter gives a detailed explanation of different deep learning methods and tries to provide a basic understanding as to how these techniques are impacting different applications in smart agriculture.

Smart Farming Using Internet of Things

2021 ◽  
pp. 236-250
Author(s):  
Rishabh Verma ◽  
Latika Kharb
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Water System ◽  
Field Conditions ◽  
Learning Models ◽  
System Framework ◽  
Smart Farming ◽  
The World ◽  
Crop Types ◽  
Smart Agriculture

Smart farming through IoT technology could empower farmers to upgrade profitability going from the amount of manure to be used to the quantity of water for irrigating their fields and also help them to decrease waste. Through IoT, sensors could be used for assisting farmers in the harvest field to check for light, moistness, temperature, soil dampness, etc., and robotizing the water system framework. Moreover, the farmers can screen the field conditions from anyplace and overcome the burden and fatigue to visit farms to confront problems in the fields. For example, farmers are confronting inconvenience while utilizing right quantity and time to use manures and pesticides in their fields as per the crop types. In this chapter, the authors have introduced a model where farmers can classify damaged crops and healthy crops with the help of different sensors and deep learning models. (i.e., The idea of implementing IoT concepts for the benefit of farmers and moving the world towards smart agriculture is presented.)

scholarly journals Deep Learning Entrusted to Fog Nodes (DLEFN) Based Smart Agriculture

2020 ◽  
Vol 10 (4) ◽  
pp. 1544 ◽  
Author(s):  
Kyuchang Lee ◽  
Bhagya Nathali Silva ◽  
Kijun Han
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Fog Computing ◽  
Multimedia Data ◽  
Network Congestion ◽  
Service Delay ◽  
Comparison Results ◽  
Smart Agriculture ◽  
Iot Devices ◽  
Computing Capacity

Colossal amounts of unstructured multimedia data are generated in the modern Internet of Things (IoT) environment. Nowadays, deep learning (DL) techniques are utilized to extract useful information from the data that are generated constantly. Nevertheless, integrating DL methods with IoT devices is a challenging issue due to their restricted computational capacity. Although cloud computing solves this issue, it has some problems such as service delay and network congestion. Hence, fog computing has emerged as a breakthrough way to solve the problems of using cloud computing. In this article, we propose a strategy that assigns a portion of the DL layers to fog nodes in a fog-computing-based smart agriculture environment. The proposed deep learning entrusted to fog nodes (DLEFN) algorithm decides the optimal layers of DL model to execute on each fog node, considering their available computing capacity and bandwidth. The DLEFN individually calculates the optimal layers for each fog node with dissimilar computational capacities and bandwidth. In a similar experimental environment, comparison results clearly showed that proposed method accommodated more DL application than other existing assignment methods and utilized resources efficiently while reducing network congestion and processing burden on the cloud.

Plant Diseases Concept in Smart Agriculture Using Deep Learning

2021 ◽  
pp. 139-153
Author(s):  
Prachi Chauhan ◽  
Hardwari Lal Mandoria ◽  
Alok Negi ◽  
R. S. Rajput
Keyword(s):  
Deep Learning ◽  
Agricultural Sector ◽  
Plant Diseases ◽  
Public Image ◽  
Economic Losses ◽  
Treatment Technique ◽  
Convolutional Networks ◽  
Novel Approach ◽  
Technological Advances ◽  
Smart Agriculture

In the agricultural sector, plant leaf diseases and harmful insects represent a major challenge. Faster and more reliable prediction of leaf diseases in crops may help develop an early treatment technique while reducing economic losses considerably. Current technological advances in deep learning have made it possible for researchers to improve the performance and accuracy of object detection and recognition systems significantly. In this chapter, using images of plant leaves, the authors introduced a deep-learning method with different datasets for detecting leaf diseases in different plants and concerned with a novel approach to plant disease recognition model, based on the classification of the leaf image, by the use of deep convolutional networks. Ultimately, the approach of developing deep learning methods on increasingly large and accessible to the public image datasets provides a viable path towards massive global diagnosis of smartphone-assisted crop disease.

Deep Learning Solutions for Agricultural and Farming Activities

2021 ◽  
pp. 256-287
Author(s):  
Asha Gowda Karegowda ◽  
Devika G. ◽  
Geetha M.
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Mortality Rate ◽  
Crop Yield ◽  
Food Supply ◽  
Automated System ◽  
Sufficient Food Supply ◽  
Smart Agriculture ◽  
Sufficient Food ◽  
Growing Population

The continuously growing population throughout globe demands an ample food supply, which is one of foremost challenge of smart agriculture. Timely and precise identification of weeds, insects, and diseases in plants are necessary for increased crop yield to satisfy demand for sufficient food supply. With fewer experts in this field, there is a need to develop an automated system for predicting yield, detection of weeds, insects, and diseases in plants. In addition to plants, livestock such as cattle, pigs, and chickens also contribute as major food. Hence, livestock demands precision methods for reducing the mortality rate of livestock by identifying diseases in livestock. Deep learning is one of the upcoming technologies that when combined with image processing promises smart agriculture to be a reality. Various applications of DL for smart agriculture are covered.

Deep Learning Automatic Inspections of Mushroom Substrate Packaging for PP-Bag Cultivations

2019 ◽  
Author(s):  
Rong-Yuan Jou ◽  
Tseng-Wei Li
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Light Sources ◽  
Clamping Force ◽  
Robot Arm ◽  
Complex Objects ◽  
3D Vision ◽  
Automatic Production ◽  
Smart Agriculture ◽  
Matching Score

Abstract The mushroom cultivation is an important smart agriculture in Taiwan. This study uses the deep learning object detection method to inspect the cap flaws or positional imperfection in the automatic production of the mushroom PP-bag packaging. This study uses the UR robotic arm and integrated 3D vision module, and uses the extra positioning axis to achieve the purpose of multi-positioning inspections by robot arm. Projecting the structured LED light sources to the object to be inspected has the advantages of a larger identification ranges and complex objects detection. A duallens CMOS industrial camera is used to capture images, and a 3D point cloud image of a basket of PP-bag packages is created by software calculation, which can obtain detailed information on the appearance of the whole basket of PP-bag packages. Deep learning is performed by the training set with labelling, and the image recognition such as the cap flaws in the PP-bag package or positional shift is performed after the training is completed. In this paper, the image data is divided into four sets of datasets, and the same training parameters are used for individual training. With images of dataset1 and the ambient illumination level of 200 lm to 800 lm, the matching score is up to 0.989. The clamping force and the opening degree are adjusted by the variable jaws. The clamping force of the jaws is maintained at 20 N to prevent the clamping force from damaging the dimensions of the PP-bag package and existing holes inside it, making the product unusable. Using the variable jaws and repeating 30 times of clamping experiments, the hole diameter inside the PP-bag package can still be maintained within around 25 mm, which can meet the needs of the mushroom PP-bag packaging.

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