{"product_id":"computer-vision-and-machine-learning-in-agriculture-volume-2-9789811699931","title":"Computer Vision and Machine Learning in Agriculture, Volume 2","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003eThis book provides an overview of advanced machine learning and deep learning tools and techniques for solving agricultural plants and products problems, with a focus on crops harvesting, weed, multi-class crops detection, yield estimation, crop maturity detection, agri-food product quality assessment, and medicinal plant recognition. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 260 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 15 March 2023\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Springer Verlag, Singapore\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eThis book serves as an extension of the previous publication, \"Computer Vision and Machine Learning in Agriculture,\" catering to the needs of academicians, researchers, and professionals engaged in addressing the challenges associated with agricultural plants and products to enhance production through the integration of advanced machine learning, including deep learning tools and techniques, into computer vision algorithms. Comprising 15 chapters, the book begins with a focus on crop harvesting, weed, and multi-class crops detection utilizing robots and UAVs, leveraging machine learning and deep learning algorithms for smart agriculture. The subsequent two chapters delve into the retrieval and collection of agricultural data. Chapters 6, 7, 8, and 9 then center on yield estimation, crop maturity detection, agri-food product quality assessment, and medicinal plant recognition, respectively. The remaining six chapters are dedicated to optimizing disease recognition through computer vision-based machine and deep learning strategies.\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003cstrong\u003eIntroduction:\u003c\/strong\u003e\u003cbr\u003eThe agricultural sector plays a crucial role in meeting the global food demand, yet it faces numerous challenges such as limited resources, climate change, and pest infestations. To address these challenges, innovative technologies, including computer vision and machine learning, have emerged as powerful tools for improving crop yields, reducing waste, and enhancing the quality of agricultural products.\u003cbr\u003e\u003cbr\u003e\u003cstrong\u003eChapter 1:\u003c\/strong\u003e\u003cbr\u003eIn this chapter, we explore the use of computer vision and machine learning in crop harvesting. We discuss the challenges faced by farmers in manual harvesting, such as labor shortages, crop damage, and low efficiency. We then introduce the concept of autonomous harvesting systems, which utilize robots and UAVs to automate the harvesting process. We discuss the various techniques used in autonomous harvesting, such as image processing, object detection, and machine learning algorithms, and their applications in different crops.\u003cbr\u003e\u003cbr\u003e\u003cstrong\u003eChapter 2:\u003c\/strong\u003e\u003cbr\u003eIn this chapter, we focus on weed detection and management in agriculture. We discuss the importance of weed control in maintaining crop yields and reducing the use of herbicides. We introduce the concept of deep learning algorithms, which have shown promising results in weed detection and classification. We discuss the various techniques used in deep learning, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and their applications in weed detection and management.\u003cbr\u003e\u003cbr\u003e\u003cstrong\u003eChapter 3:\u003c\/strong\u003e\u003cbr\u003eIn this chapter, we explore the use of computer vision and machine learning in multi-class crops detection. We discuss the challenges faced by farmers in identifying and classifying different crops, which can be time-consuming and labor-intensive. We introduce the concept of deep learning algorithms, which have the ability to learn and recognize complex patterns in images. We discuss the various techniques used in deep learning, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and their applications in multi-class crops detection.\u003cbr\u003e\u003cbr\u003e\u003cstrong\u003eChapter 4:\u003c\/strong\u003e\u003cbr\u003eIn this chapter, we discuss the retrieval and collection of agricultural data. We discuss the importance of accurate and timely data in decision-making in agriculture. We introduce the concept of big data analytics, which involves the processing, analysis, and visualization of large datasets to extract valuable insights. We discuss the various techniques used in big data analytics, such as data mining, machine learning, and cloud computing, and their applications in agricultural data retrieval and collection.\u003cbr\u003e\u003cbr\u003e\u003cstrong\u003eChapter 5:\u003c\/strong\u003e\u003cbr\u003eIn this chapter, we focus on yield estimation in agriculture. We discuss the challenges faced by farmers in predicting crop yields, which can be influenced by various factors such as weather, soil quality, and pest infestations. We introduce the concept of machine learning algorithms, which have the ability to analyze large datasets and make accurate predictions. We discuss the various techniques used in machine learning, such as regression analysis, decision trees, and support vector machines, and their applications in yield estimation.\u003cbr\u003e\u003cbr\u003e\u003cstrong\u003eChapter 6:\u003c\/strong\u003e\u003cbr\u003eIn this chapter, we discuss the crop maturity detection in agriculture. We discuss the importance of timely detection of crop maturity in optimizing crop yields and reducing waste. We introduce the concept of computer vision algorithms, which can analyze images of crops to detect the stage of maturity. We discuss the various techniques used in computer vision, such as image processing, object detection, and deep learning, and their applications in crop maturity detection.\u003cbr\u003e\u003cbr\u003e\u003cstrong\u003eChapter 7:\u003c\/strong\u003e\u003cbr\u003eIn this chapter, we discuss the agri-food product quality assessment in agriculture. We discuss the challenges faced by farmers in assessing the quality of agricultural products, which can be influenced by factors such as color, texture, and flavor. We introduce the concept of computer vision algorithms, which can analyze images of agricultural products to detect defects and quality issues. We discuss the various techniques used in computer vision, such as image processing, object detection, and deep learning, and their applications in agri-food product quality assessment.\u003cbr\u003e\u003cbr\u003e\u003cstrong\u003eChapter 8:\u003c\/strong\u003e\u003cbr\u003eIn this chapter, we discuss the medicinal plant recognition in agriculture. We discuss the importance of identifying and harvesting medicinal plants for their therapeutic properties. We introduce the concept of computer vision algorithms, which can analyze images of plants to identify specific characteristics and classify them as medicinal plants. We discuss the various techniques used in computer vision, such as image processing, object detection, and deep learning, and their applications in medicinal plant recognition.\u003cbr\u003e\u003cbr\u003e\u003cstrong\u003eChapter 9:\u003c\/strong\u003e\u003cbr\u003eIn this chapter, we discuss the optimized disease recognition in agriculture. We discuss the challenges faced by farmers in identifying plant diseases, which can be influenced by various factors such as weather, soil quality, and pest infestations. We introduce the concept of computer vision-based machine and deep learning strategies, which involve the integration of computer vision and machine learning algorithms to optimize disease recognition. We discuss the various techniques used in computer vision-based machine and deep learning, such as image processing, object detection, and deep learning, and their applications in optimized disease recognition.\u003cbr\u003e\u003cbr\u003e\u003cstrong\u003eConclusion:\u003c\/strong\u003e\u003cbr\u003eIn conclusion, this book serves as an invaluable resource for academicians, researchers, and professionals interested in solving the problems of agricultural plants and products for boosting production by rendering the advanced machine learning including deep learning tools and techniques to computer vision algorithms. The book provides a comprehensive overview of the current state-of-the-art in computer vision and machine learning in agriculture, and it highlights the potential applications of these technologies in various sectors of the agriculture industry. By leveraging the power of computer vision and machine learning, we can enhance the efficiency, productivity, and sustainability of agricultural practices, ultimately contributing to a more food-secure and sustainable world.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 427g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 235 x 155 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9789811699931\u003cbr\u003e \u003cstrong\u003eEdition number\u003c\/strong\u003e: 1st ed. 2022\u003c\/p\u003e","brand":"Shulph Ink","offers":[{"title":"Paperback \/ softback","offer_id":44307644907770,"sku":"9789811699931","price":116.61,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/noImage_1_75bdf93d-77cf-4632-9da8-c924eaf1548c.jpg?v=1688110969","url":"https:\/\/shulphink.com\/products\/computer-vision-and-machine-learning-in-agriculture-volume-2-9789811699931","provider":"Shulph Ink","version":"1.0","type":"link"}