مجله علوم و فناوری اطلاعات کشاورزی
پیاپی 13 (بهار و تابستان 1403)

Identifying, predicting and managing crisis in a climate structure is of great importance. Models are used as practical tools for understanding complex systems and simulating and predicting their behavior. Support vector machines are one of the supervised learning methods used for classification and regression. Support vector machines are able to detect hidden patterns and respond to complex changes in climate data. In this article, the structure of the support vector machine method and its application in climate data classification are presented. The characteristics of the structure of support vector machines are related to the selection of the kernel function type, so sufficient care must be taken in the selection of the kernel function type And on the other hand, PCI in climate forecasting is an important step in climate forecasting in order to make the best fit between forecasting and predicted data with the optimal number of parameters.

Artificial intelligence produces intelligent machines capable of performing tasks that require human intelligence. GPT is an advanced language processing model based on artificial intelligence that produces human-like texts based on deep learning algorithms depending on input data. Chat GPT is a "chatbot" that answers any question and understands complex topics in specialized fields with its outstanding hybrid and linguistic capabilities. This article examines the possibility of using Chat GPT in providing library reference and information services. It points out the benefits and potential challenges of its use in response time, quality of responses, and librarian workload. Although Chat GPT can be a valuable tool in providing library reference services, especially in answering simple questions, it cannot substitute for competent reference librarians since librarians offer more complex answers by interacting constructively with users.

Accurate detection of apple leaf diseases is essential to prevent the reduction in both the quantity and quality of crop yield. With advancements in deep learning methods, the diagnosis of these diseases is improving, but data limitations remain a significant barrier. This research evaluates pretrained deep learning models with precise settings and demonstrates that high accuracy in disease detection is possible even with limited data. The selected models perform better than conventional methods and introduce transfer learning as an effective strategy to combat limited data and the diversity of diseases. These models aid farmers and horticulture specialists in identifying and managing apple leaf diseases more efficiently and rapidly. Additionally, these models are not only beneficial for farmers but also for agricultural consultants, students of agricultural sciences, and researchers in this field. Moreover, these methods are adaptable to other diseases and plants, promising advancements in plant disease detection systems in the future. The present study has the potential to revolutionize horticultural processes through optimization and automation, leading to a transformation in plant disease management.

The purpose of this article is to get a general picture of the technology of solar farms in the integration of the agricultural sector and energy resources, along with the challenges and benefits of its use. Solar farm systems offer a symbiotic strategy for the coexistence of sustainable renewable energy and agricultural production. As emphasized in the food-energy-water nexus, solar farm developments should focus on agronomic management (crop and water management) in addition to energy management. The structure of solar farms and the advances of crop management depend on the quality of solar radiation in terms of light intensity and photosynthetic active radiation, the categories of solar farms (such as energy-oriented, agriculture-oriented, and agriculture-energy-oriented) and the perspective of stakeholders (especially farmers). Also, due to light limitation, microclimate conditions and solar structure limitations, several adjustments are required for crop selection and management. More importantly, a systematic irrigation system is required to prevent damage to the solar panel structure. In summary, solar farm developments must be carefully planned to ensure the goals of reducing reliance on non-renewable resources, mitigating the effects of global warming, and meeting food-energy-water initiatives.

The article illuminates the evolutionary transformation of science and technology parks and innovation districts and evaluates the impact of such transformations on cities and rural areas' economic and social development. Due to the growing demand for innovative products and services, along with an increase in the workforce and expanded higher education opportunities globally, favorable conditions exist in the global and national markets for benefiting from the capacity of knowledge-based businesses. There is an emphasis on the significance of open collaborations, mutual relationships, and knowledge flow between relevant companies and organizations in the innovation and technology ecosystem. Science and technology parks play a critical role in knowledge exchange and wealth generation by creating, establishing, developing, and supporting innovative working groups and start-up businesses. However, the independent and isolated activities of science and technology parks hinder their potential to develop the innovation and technology ecosystem. It is essential to establish effective relationships and collaborations between knowledge-generating organizations, commercial companies, and innovation organizations to enable the residents to benefit from technological and innovative amenities in urban and rural areas. Innovation districts are the new generation of science and technology parks at national scales, offering a novel approach to knowledge-based urban and rural development.

Book publishing is one of the measures of progress in any society. The main objective of the current research is to investigate the status of book publishing by the Department of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad (FUM) during nearly half a century of its establishment. The statistical population of the research includes all the books published by the Department since establishment in 1975 to 2022. A checklist was used to collect information and data were analyzed using descriptive statistics. The research findings showed that during the study period, 196 book titles were published in the Department, of which 79.6% and 20.4% were authored and translated, respectively. In total, 34 publishers contributed to the publication of books. The most significant number of books (85 titles) have been published by the FUM Press. Books on “Agroecology” were more frequent than books on other subjects, with a frequency of 27%. The most prolific member of the Department is Professor Alireza Koocheki, who authored or translated 88 books. In general, due to the newness of the major of “Seed Science and Technology” in the Department, it is necessary to pay more attention to translating and authoring books related to this subject

The agricultural industry must pay attention to the issues of population increase, climate change and natural resource destruction in a way that is responsive to food and environmental needs. Paying attention to the Internet of Things can be one of them. Internet of things means a set of objects that are connected through the network. These objects can be sensors monitoring environmental factors such as temperature, humidity, light, soil moisture, soil acidity and carbon dioxide concentration, or types of agricultural equipment. In this article, the types of sensors and their capacity to intelligentize different aspects of agriculture (such as irrigation, nutrition, fertilization, disease diagnosis, monitoring and climate control) according to the facilities and capacities of the Internet of Things are introduced, so that they can be used to increase agricultural productivity, preserving resources and reduce human errors.