Application of Deep and Machine Learning in Crop Monitoring and Management

Dear Colleagues,

The application of deep and machine learning in crop monitoring and management has become increasingly important in light of the growing demand for sustainable agricultural practices. While traditional methods provide valuable insights into crop management, the integration of deep and machine learning techniques into existing approaches offers a unique opportunity to improve the efficiency and sustainability of agriculture. By incorporating deep and machine learning analytics, various crop-related parameters such as growth patterns, soil composition and fertilization, crop productivity, climate conditions, pest infestations, and many other issues in modern agriculture can be assessed with greater predictive accuracy. This enables the comprehensive monitoring and management of crops in different agricultural landscapes, from small farms to large plantations. Deep learning algorithms can recognize complex patterns in large data sets when monitoring crops, facilitating informed decision-making processes. By analyzing satellite imagery, sensor data and historical records, or in situ field research data, deep and machine learning models can predict crop yields, identify areas prone to disease outbreaks, and optimize resource allocation for higher productivity.

This Special Issue aims to expand current knowledge on crop monitoring and management assessment using deep and machine learning methods in various agricultural fields. Contributions should cover a broad range of topics that serve as cornerstones for optimizing crop management, with deep and machine learning serving as the primary analytical approaches. Examples of potential topics include precision agriculture, remote sensing applications, environmental impact assessment, climate change in agriculture, biotic and abiotic factors of agricultural production, and other interdisciplinary areas important to crop monitoring and management. We strongly encourage the submission of original research articles and reviews to showcase the versatility of deep and machine learning in crop monitoring and management and to provide professionals worldwide with insights into refining techniques and evaluating criteria in their respective fields.

It is our great pleasure to invite you to the Special Issue "Application of Deep and Machine Learning in Crop Monitoring and Management", which aims to bring together the application of state-of-the-art, efficient, and flexible deep and machine learning methods to determine optimal strategies for crop monitoring and management.

We look forward to receiving your contributions!

Dr. Vjekoslav Tadić
Dr. Dorijan Radočaj
Prof. Dr. Mladen Jurišić
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• deep and machine learning
• crop productivity
• prediction of crop related parameters
• prediction of biotic and abiotic factors in agricultural production
• soil composition and fertilization
• climate change impact of agriculture
• pest management
• precision agriculture
• remote sensing applications
• convolutional neural networks (CNNs)
• unmanned aerial vehicles (UAVs)
• phenotyping
• data fusion
• decision support systems