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题名/责任者:

Application of machine learning in agriculture / edited by Mohammad Ayoub Khan (College of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia), Rijwan Khan (Department of Computer Science and Engineering, ABES Institute of Technology (Affiliated to AKTU Lucknow), Ghaziabad, Uttar Pradesh, India), Mohammad Aslam Ansari (Department of Agriculture Communication College of Agriculture, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India).

出版发行项:

Amsterdam : Academic Press, an imprint of Elsevier, [2022]

ISBN:

9780323905503

载体形态项:

xvi, 314 pages : illustrations (some color) ; 24 cm.

附加个人名称:

Khan, Mohammad Ayoub, 1980- editor.

附加个人名称:

Khan, Rijwan, 1981- editor.

附加个人名称:

Ansari, Mohammad Aslam, editor.

论题主题:

Artificial intelligence-Agricultural applications.

论题主题:

Machine learning.

中图法分类号:

S126

书目附注:

Includes bibliographical references and index.

内容附注:

Front Cover -- Application of Machine Learning in Agriculture -- Copyright Page -- Contents -- List of contributors -- 1 Fundamentals of smart agriculture -- 1 Machine learning-based agriculture -- Introduction -- Literature review -- Deep learning in agriculture -- Transfer learning for pest detection -- Proposed method -- Pest detection -- Performance evaluation -- Crop yield prediction -- Methodology -- E-Mandi -- Methodology -- Functionalities -- Comparative study -- Comparison between different pest detection techniques -- Comparison between different crop yield prediction techniques -- Results and discussions -- Pest detection -- Crop yield prediction -- E-Mandi -- Conclusion -- References -- 2 Monitoring agricultural essentials -- Introduction -- Unsupervised machine learning algorithms for agriculture -- Supervised machine learning algorithms for agriculture -- Artificial neural network -- Linear regression -- Random forest -- Proposed predictive model for agriculture -- Data collection devices -- Data storage -- Crop prediction -- Real-time monitoring -- Communication technology -- Results and discussion -- Summary -- References -- 3 Machine learning-based remote monitoring and predictive analytics system for monitoring and livestock monitoring -- Introduction -- Motivation -- Background study -- Techniques used -- Artificial intelligence -- Machine learning -- Benefits of the work -- Research challenges -- Role of artificial intelligence and machine learning for crop monitoring -- Reported work -- Comparative analysis -- Conclusion -- References -- 2 Market, Technology and Products -- 4 Agricultural economics -- Introduction -- Prediction of crop price -- Impact of gross domestic product -- Share of agriculture in gross domestic product -- Government schemes.

内容附注:

Economical changes in traditional agriculture versus machine learning agriculture -- Meteorology -- Scope of agrometeorology -- Agrometeorology's relationship with agricultural sciences -- Difference between meteorology and agrometeorology -- Crops and animals using agrometeorology -- Crops -- Animals -- Agrometeorology as an interdisciplinary science -- Conclusion -- References -- 5 Current and prospective impacts of digital marketing on the small agricultural stakeholders in the developing countries -- Introduction -- Definition of and types of electronic business -- Digital agricultural market before, during, and what is expected after the COVID-19 pandemic in developing countries -- Digital agricultural market to mitigate the negative impacts of uncertainty -- Opportunities and risks of investment in the digital agricultural market industry -- Market segmentation of the digital agricultural market in developing countries -- A mobile banking system -- Digital agricultural value chain and its stakeholders -- Impacts of digital agriculture on poverty reduction, food security rates, and food losses and waste reduction in developing... -- Agricultural digitalization to achieve the sustainable development goals 2030 -- Conclusion -- References -- 6 Intelligent farming system through weather forecast support and crop production -- Introduction -- Technology stack used -- Internet of Things -- Used algorithms -- Deep neural networks -- Random forest program -- System-related architecture -- Raspberry Pi 3 -- Sensor module based on DHT 11 -- Sensor for soil moisture -- Sensor for sensing rainfall -- Sensor BMP-180 type -- Software Raspbian (Raspberry pi) -- ThingSpeak -- Jupyter Notebook -- Weather prediction -- Predicting temperature -- Methodology used -- Results -- Conclusions -- References.

内容附注:

7 Deep learning-based prediction for stand age and land utilization of rubber plantation -- Introduction -- Background and related work -- Rubber land-use mapping and age estimation via remote sensing imagery -- Feature representation -- Supervised classification methods -- Study materials -- Details of the area of study -- Dataset -- Solution design and implementation -- Model design -- Data preprocessing -- Supervised learning process -- VGG-16 and FCN-8 model -- U-net model -- Model evaluation -- Experimental setup -- Classification performance analysis -- Vegetation index analysis -- Discussion -- Study contributions -- Study challenges -- Future research directions -- Conclusion -- Acknowledgment -- References -- 3 Tools and Techniques -- 8 Modeling techniques used in smart agriculture -- Introduction -- Expert system -- Fuzzy framework for smart agriculture -- Fuzzy system architecture -- Knowledge base -- Fuzzy framework -- Fuzzification -- Fuzzy inference engine -- Defuzzification -- Conclusion -- References -- 9 Plant diseases detection using artificial intelligence -- Introduction -- Literature survey -- Recognizing plant diseases -- Image acquisition -- Image preprocessing -- Image segmentation -- Region based -- Edge based -- Threshold based -- Feature-based clustering -- Feature extraction -- Image recognition -- Performance measures for image recognition techniques -- Discussion and future work -- Conclusion -- References -- 10 A deep learning-based approach for mushroom diseases classification -- Introduction -- Related works -- Dataset description -- Methods -- Image augmentation -- Noise removal from image -- Image enhancement technique -- Deep learning algorithm -- AlexNet -- ResNet15 -- GoogleNet -- Result analysis and discussion -- Conclusion -- References -- 11 Smart fence to protect farmland from stray animals -- Introduction.

内容附注:

Agricultural fences -- Natural repellents -- Smart fence to protect farmland -- Virtual fence setup using optical fiber sensor -- Optical fiber cable -- Types of optical fibers -- Optical fiber cable as sensor -- Fiber optic sensors -- Types of fiber-optic sensor systems -- Intrinsic type fiber optic sensors -- Extrinsic type fiber-optic sensors -- Classification of fiber-optic sensors on the basis of operating principles -- Intensity-based fiber-optic sensor -- Polarization-based fiber-optic sensor -- Phase-based fiber optic sensor -- Signal analysis -- Gait-based analysis to differentiate walking, running, and tapping -- Algorithm for classification -- Results -- Conclusion -- References -- 12 Enhancing crop productivity through autoencoder-based disease detection and context-aware remedy recommendation system -- Introduction -- Preliminaries -- Multilayer perceptron -- Stacked denoising autoencoder -- Convolutional neural network -- Sentiment analysis -- Proposed method -- Disease detection phase -- Cascading autoencoder -- Threshold module -- Infection region detector -- Disease classification phase -- Recommendation phase -- Stacked autoencoder -- Experimental valuation -- Description of the dataset -- Implementation details -- Evaluation metric -- Performance of segmentation by cascading autoencoder -- Performance of classification by concise convolutional neural network -- Performance of remedy recommendation stacked autoencoder (SAE) -- Conclusion -- References -- 13 UrbanAgro: Utilizing advanced deep learning to support Sri Lankan urban farmers to detect and control common diseases in... -- Introduction -- Literature review -- Preprocessing -- Feature extraction -- Machine learning -- Deep learning -- Classification -- Object detection -- Implementation -- Methodology -- Data collection -- Image annotation -- Data augmentation.

内容附注:

Deep learning model -- YOLOv3 design -- Justification for choosing an object detection model over a classification model -- Model Training -- Results and discussion -- Comparison of our results with previous works -- Conclusion -- Limitations -- Future directions -- Acknowledgments -- References -- 14 Machine learning techniques for agricultural image recognition -- Introduction -- Steps for image analysis -- Machine learning strategies in agricultural image recognition -- Traditional machine learning methods -- Supervised learning algorithms -- Classification methods -- Support vector machines -- Naive Bayes -- K-nearest neighbors -- Decision tree -- Random forest -- Artificial neural networks -- Regression methods -- Linear regression (LR) -- Multiple linear regression -- Support vector regression -- Ensemble -- Unsupervised learning algorithm -- k-Means -- Fuzzy clustering -- Gaussian -- Reinforcement learning -- Deep learning models -- Convolutional neural networks -- Restricted Boltzmann machine -- Long-term short memory -- Applications of image processing in agriculture tasks -- Soil assessment -- Irrigation -- Leaf analysis -- Weed detection -- Pest control -- Disease detection -- Vegetation measurement -- Monitoring plant growing -- Fruit/food grading -- Crop yield -- Flower and seed detection -- Plant classification -- Summary -- References -- Index -- Back Cover.

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