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E-raamat: Deep Learning for Sustainable Agriculture

Edited by (Associate Professor, Department of Computer Science and Engineering, Manipal University Jaipur, India), Edited by (Assistant Professor, ), Edited by (Professor, Department of Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka, India)
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Deep Learning for Sustainable AgricultureSuurem pilt
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Deep Learning for Sustainable Agriculture reviews the fundamental concepts of gathering, processing and analyzing different deep learning models, along with a review of methods that can be used in this direction. The book also covers novel deep learning techniques for effective agriculture data management with standards laid by international organizations in related fields. The book is centered around evolving novel intelligent/deep learning models to solve the mitigation of agriculture. There are several deep learning models known that are used for weather forecasting, plant disease detection, underground water detection, quality of soil, and many more issues in agriculture.

  • Introduces the novel deep learning models needed to address sustainable solutions for issues related to agriculture
  • Provides reviews on the latest intelligent technologies and algorithms related to the state-of-the-art methodologies of monitoring and mitigation of sustainable agriculture
  • Offers perspectives for the design, development and commissioning of intelligent applications
Contributors xiii
1 Smart agriculture: Technological advancements on agriculture---A systematical review
Chanki Pandey
Prabira Kumar Sethy
Santi Kumari Behera
Jaya Vishwakarma
Vishal Tande
1 Introduction
1(3)
2 Methodology
4(1)
3 Role of image processing in agriculture
5(4)
3.1 Plant disease identification
5(1)
3.2 Fruit sorting and classification
6(1)
3.3 Plant species identification
7(1)
3.4 Precision farming
7(1)
3.5 Fruit quality analysis
8(1)
3.6 Crop and land assessment
8(1)
3.7 Weed recognition
8(1)
4 Role of Machine Learning in Agriculture
9(7)
4.1 Yield prediction
13(1)
4.2 Disease detection
14(1)
4.3 Weed recognition
15(1)
4.4 Crop quality
15(1)
4.5 Species recognition
15(1)
4.6 Soil management
16(1)
5 Role of deep learning in agriculture
16(9)
5.1 Leaf disease detection
16(5)
5.2 Plant disease detection
21(1)
5.3 Land cover classification
22(1)
5.4 Crop type classification
22(1)
5.5 Plant recognition
22(1)
5.6 Segmentation of root and soil
23(1)
5.7 Crop yield estimation
23(1)
5.8 Fruit counting
23(1)
5.9 Obstacle detection
24(1)
5.10 Identification of weeds
24(1)
5.11 Prediction of soil moisture
25(1)
5.12 Cattle race classification
25(1)
6 Role of IoT in agriculture
25(7)
6.1 Climate condition monitoring
30(1)
6.2 Crop yield
30(1)
6.3 Soil patter
30(1)
6.4 Pest and crop disease monitoring
30(1)
6.5 Irrigation monitoring system
31(1)
6.6 Optimum time for plant and harvesting
31(1)
6.7 Tracking and tracing
31(1)
6.8 Farm management system
32(1)
6.9 Agricultural drone
32(1)
7 Role of wireless sensor networks in agriculture
32(4)
7.1 Irrigation management
35(1)
7.2 Soil moisture prediction
35(1)
7.3 Precision farming
35(1)
7.4 Climate condition monitoring
36(1)
8 Role of data mining in agriculture
36(6)
8.1 Irrigation management
39(1)
8.2 Prediction and detection of plant diseases
39(1)
8.3 Pest monitoring
40(1)
8.4 Optimum management of inputs (fertilizer and pesticides)
40(1)
8.5 Crop yield prediction
41(1)
8.6 Climate condition monitoring
42(1)
9 Conclusion
42(5)
References
47(10)
2 A systematic review of artificial intelligence in agriculture
Parvinder Singh
Amandeep Kaur
1 Precision farming
57(8)
1.1 Introduction
57(2)
1.2 Related work using AI
59(2)
1.3 Objective and design consideration
61(2)
1.4 Challenges and future scope
63(2)
2 Plant disease detection
65(8)
2.1 Introduction
65(2)
2.2 Deep learning in image processing
67(2)
2.3 Review of plant disease detection using image processing and deep learning
69(1)
2.4 Performance analysis of some state-of-art techniques
70(1)
2.5 Research gaps and future scope
70(3)
3 Soil health monitoring using AI
73(1)
3.1 Introduction
73(1)
3.2 Brief history
73(1)
3.3 Opportunity of AI in soil health monitoring
74(1)
3.4 Current status
74(1)
4 Scope and challenges of AI in agriculture
74(1)
5 Conclusions
75(1)
References
75(6)
3 Introduction to deep learning in precision agriculture: Farm image feature detection using unmanned aerial vehicles through classification and optimization process of machine learning with convolution neural network
Halimatu Sadiyah Abdullahi
Ray E. Sheriff
1 Introduction
81(3)
2 Deep learning overview
84(3)
3 CNN training
87(1)
3.1 CNN in agricultural applications
87(1)
4 Methodology
88(9)
4.1 Data collection and processing
88(1)
4.2 UAV specification
89(1)
4.3 Image processing and labeling
90(7)
5 Experiment and results
97(4)
5.1 Binary classification
97(2)
5.2 Multiclass classification
99(2)
6 Discussion
101(2)
6.1 Advantages of the developed model
103(1)
7 Conclusion
103(1)
References
104(5)
4 Design and implementation of a crop recommendation system using nature-inspired intelligence for Rajasthan, India
Lavika Goel
Akshina Jindal
Shray Mathur
1 Introduction
109(1)
2 Literature survey
110(1)
3 Proposed methodology
111(11)
3.1 Preprocessing layer
111(1)
3.2 Feature extraction
111(2)
3.3 Optimization layer
113(8)
3.4 Softmax classification layer
121(1)
4 Results
122(4)
5 Conclusion and future work
126(1)
References
127(1)
Further reading
127(2)
5 Artificial intelligent-based water and soil management
Ahmed Elbeltagi
Nand Lal Kushwaha
Ankur Srivastava
Amira Talaat Zoof
1 Introduction
129(1)
2 Applications of artificial intelligence in water management
130(5)
2.1 Evapotranspiration estimation
130(2)
2.2 Crop water content prediction
132(1)
2.3 Water footprint modeling
132(1)
2.4 Groundwater simulation
132(2)
2.5 Pan evaporation estimation
134(1)
3 Applications of artificial intelligence in soil management
135(3)
3.1 Soil water content determination
136(1)
3.2 Soil temperature monitoring
136(1)
3.3 Soil fertilizer estimation
137(1)
3.4 Soil mapping
137(1)
4 Conclusion and recommendations for water-soil management
138(1)
References
138(5)
6 Machine learning for soil moisture assessment
Alka Rani
Nirmal Kumar
Jitendra Kumar
Jitendra Kumar
Nishant K. Sinha
1 Introduction
143(2)
2 Overview of machine learning
145(1)
3 Machine learning algorithms applied in soil moisture research
146(5)
3.1 Linear regression
146(1)
3.2 Artificial neural network/deep neural network
147(1)
3.3 Support vector machine
148(1)
3.4 Classification and regression tree
149(1)
3.5 Random forest
150(1)
3.6 Extremely randomized trees
150(1)
4 Applications of machine learning for soil moisture assessment
151(8)
4.1 Pedotransfer functions
151(1)
4.2 Prediction models for soil moisture estimation/forecasting
152(1)
4.3 Soil moisture retrieval through remote sensing
153(3)
4.4 Irrigation scheduling
156(1)
4.5 Downscalingof satellite-derived soil moisture products
157(2)
5 Conclusions
159(3)
Abbreviations
162(1)
References
163(6)
7 Automated real-time forecasting of agriculture using chlorophyll content and its impact on climate change
K. Sujatha
R.S. Ponmagal
K. Senthil Kumar
Rajeswary Hari
A. Kalaivani
K. Thivya
M. Anand
1 Introduction
169(3)
2 Current status
172(3)
2.1 National Status
174(1)
2.2 International status
175(1)
3 Problem statement
175(1)
4 Objective of the proposed work
176(1)
5 Research highlights
177(1)
6 Scientific significance of the proposed work
177(1)
7 Materials and methods
178(3)
7.1 Histogram of oriented gradients
178(1)
7.2 Principal component analysis
179(1)
7.3 Backpropagation algorithm
179(2)
8 Detailed work plan to achieve the objectives
181(2)
8.1 Methodology
182(1)
9 Results and discussion
183(5)
10 Conclusion
188(8)
References
196(3)
8 Transformations of urban agroecology landscape in territory transition
Jose C. Vargas-Hernandez
1 Introduction
199(1)
2 Agroecological landscapes
200(1)
3 Agroecological practices
201(6)
4 Agroecological territorial transformation and transition
207(7)
5 Conclusion
214(1)
References
215(8)
9 WeedNet: A deep neural net for weed identification
Shashi Prakash Tripathi
Rahul Kumar Yadav
Harshita Rai
1 Introduction
223(2)
2 Related work
225(1)
3 WeedNet
226(3)
3.1 Model architecture
226(2)
3.2 Complexity analysis
228(1)
4 Evaluation strategy
229(2)
4.1 Performance metrics
229(2)
4.2 Data set
231(1)
5 Experimental setup
231(1)
6 Experimental evaluation
231(1)
7 Conclusion
231(3)
References
234(3)
10 Sensors make sense: Functional genomics, deep learning, and agriculture
Ross McDougal Henderson
Claudia Rossi
Michelle Burgess
1 Introduction
237(2)
2 Section I. Functional genomics
239(20)
2.1 The emerging applications of soil microbial metabolites
239(1)
2.2 Agricultural-based metabolites to advance nutraceutical production and drug discovery
240(4)
2.3 Marine microalgae, aquaculture, and the DL toolbox LTjdwig
244(11)
2.4 Pollinators, Ludwig combiners, and the carbon-energy cycle
255(4)
3 Section II. DAS networks
259(8)
3.1 Agricultural factors in the plant-silicon cycle: Genomic regulation of blight, drought, and invasive species
259(1)
3.2 Helically wound DAS
260(7)
4 Section III. GRANITE and the agent-based GRANITE Network Discovery Tool
267(3)
5 Conclusions
270(1)
Acknowledgments
270(1)
References
270(3)
11 Crop management: Wheat yield prediction and disease detection using an intelligent predictive algorithms and metrological parameters
Nandini Babbar
Ashish Kumar
Vivek Kumar Verma
1 Introduction
273(3)
2 Literature review
276(14)
2.1 Wheat yield prediction
276(9)
2.2 Wheat diseases detection
285(5)
3 Discussion
290(1)
4 Conclusion and future scope
290(2)
References
292(5)
12 Sugarcane leaf disease detection through deep learning
N.K. Hemalatha
R.N. Brunda
G.S. Prakruthi
B.V. Balaji Prabhu
Arpit Shukla
Omkar Subbaram Jois Narasipura
1 Introduction
297(2)
2 Methodology
299(6)
2.1 Dataset
299(1)
2.2 Leaf disease detection system architecture
300(1)
2.3 Leaf disease detection model architecture
301(2)
2.4 SAFAL-FASAL android application
303(1)
2.5 Method of evaluation
304(1)
3 Experimentation
305(3)
4 Results and discussion
308(14)
4.1 Performance evaluation
318(1)
4.2 SAFAL-FASAL Android application results
318(4)
5 Conclusion
322(1)
References
322(3)
13 Prediction of paddy cultivation using deep learning on land cover variation for sustainable agriculture
D.A. Meedeniya
I. Mahakalanda
D.S. Lenadora
I. Perera
S.G.S. Hewawalpita
C. Abeysinghe
Soumya Ranjan Nayak
1 Introduction
325(2)
2 Applications of geospatial analytics for agriculture
327(7)
2.1 Importance of remote sensing to estimate paddy area
327(1)
2.2 Related studies based on satellite imaginary
328(2)
2.3 Related studies based on the Internet of Things
330(1)
2.4 Related studies with integrated data
330(1)
2.5 Dataset associated with land-use land-cover data
331(1)
2.6 Comparison of related studies with satellite imagery and deep learning
331(3)
3 Material analysis
334(3)
3.1 Data source
334(1)
3.2 Analysis of raster data
335(2)
4 System model design and implementation
337(4)
4.1 Process view
337(1)
4.2 Data preprocessing and feature selection
337(3)
4.3 Transfer learning process
340(1)
5 System evaluation
341(7)
5.1 Model evaluation
341(1)
5.2 Ground truth measurement
341(2)
5.3 Model prediction comparison for contextual analysis
343(5)
6 Discussion
348(3)
6.1 Contribution of the proposed study
348(1)
6.2 Limitations of the datasets
349(1)
6.3 Future research directions
350(1)
7 Conclusions
351(1)
References
352(5)
14 Artificial intelligence-based detection and counting of olive fruit flies: A comprehensive survey
Nariman Mamdouh
Mohamed Wael
Ahmed Khattab
1 Introduction
357(2)
2 Literature survey of recognition systems
359(14)
2.1 Manual detection and counting
360(1)
2.2 Semiautomatic detection and counting
361(2)
2.3 Automatic detection and counting
363(10)
3 Evaluation and discussions
373(5)
3.1 Semiautomatic detection
373(1)
3.2 Image-based automatic detection
374(3)
3.3 Nonimage-based automatic detection
377(1)
4 Conclusions
378(1)
Acknowledgments 378(1)
References 378(3)
Index 381
Dr. Ramesh Chandra Poonia is a Professor at the Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India. Recently completed his Postdoctoral Fellowship from CPS Lab, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Ålesund, Norway. He has received his Ph.D. degree in Computer Science from Banasthali University, Banasthali, India in July 2013. His research interests are Cyber-Physical Systems, Network Protocol Evaluation and Artificial Intelligence. He is Chief Editor of TARU Journal of Sustainable Technologies and Computing (TJSTC) and Associate Editor of the Journal of Sustainable Computing: Informatics and Systems, Elsevier. He also serves in the editorial boards of a few international journals. He is main author and co-author of 06 books and an editor of more than 25 special issue of journals and books including Springer, CRC Press Taylor and Francis, IGI Global and Elsevier, edited books and Springer conference proceedings and has authored/co-authored over 65 research publications in peer-reviewed reputed journals, book chapters and conference proceedings. Dr. Vijander Singh is working as Assistant Professor, Department of Computer Science and Engineering, Manipal University Jaipur, India. He received Ph.D. degree from Banasthali University, Banasthali, India, in April 2017. He has published 25 research papers in indexed journals and several book chapters for international publishers. He authored two books and handled/handling journals of international repute such as Taylor & Francis, Taru Publication, IGI Global, Inderscienc, etc. as guest editor. He is an associate editor of TARU Journal of Sustainable Technologies and Computing (TJSTC). He has organized several International Conferences, FDPs, and Workshops as a core team member of the organizing committee. His research area includes Machine Learning, Deep Learning, Precision Agriculture, and Networking. Dr. Soumya Ranjan Nayak now holds the position of Assistant Professor in the School of Computer Engineering at Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, located in Odisha, India. He obtained a Doctor of Philosophy (Ph.D) and Master of Technology (M.Tech) in Computer Science and Engineering under a scholarship provided by the Ministry of Human Resource Development (MHRD) of the Government of India. These degrees were earned at CET, BPUT Rourkela, India. Prior to this, he completed a Bachelor of Technology (B. Tech) and a Diploma in Computer Science and Engineering. He has authored over 150 articles that have been published in reputable international journals and conferences such as Elsevier, Springer, World Scientific, IOS Press, Taylor & Francis, Hindawi, Inderscience, IGI Global, and others. These publications have undergone a rigorous peer-review process. In addition to the aforementioned accomplishments, the individual has authored 16 book chapters, published 6 books, and obtained 7 Indian patents (with 4 patents being granted). Furthermore, they have secured 4 International patents, all of which have been granted. The researcher's current areas of focus encompass medical picture analysis and classification, machine learning, deep learning, pattern recognition, fractal graphics, and computer vision. The author's writings have garnered over 1500 citations, with an h-index of 24 and an i10-index of 63, as reported by Google Scholar. Dr. Nayak holds the position of an associate editor for several esteemed academic journals, including the Journal of Electronic Imaging (SPIE), Mathematical Problems in Engineering (Hindawi), Journal of Biomedical Imaging (Hindawi), Applied Computational Intelligence and Soft Computing (Hindawi), and PLOS One. He is currently fulfilling the role of a guest editor for special issues of renowned academic journals such as Springer Nature, Elsevier, and Taylor & Franchise. He has been affiliated as a reviewer for numerous esteemed peer-reviewed journals, including Applied Mathematics and Computation, Journal of Applied Remote Sensing, Mathematical Problems in Engineering, International Journal of Light and Electron Optics, Journal of Intelligent and Fuzzy Systems, Future Generation Computer Systems, Pattern Recognition Letters, and others. He has additionally held the Technical Program Committee Member position for several conferences of significant worldwide recognition.
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