3 pages, Horticulture crops play significant role in improving the productivity of land, generating employment, enhancing exports, improving economic conditions of the farmers and entrepreneurs and providing food and nutritional security to the people. For better management of the existing crops and to bring more area under horticulture crops, updated and accurate database is necessary for systematic planning and decision making. Remote sensing (RS) is an advanced tool that aids in gathering and updating information to develop scientific management plans. Many types of sensors namely microwave radiometers, laser meters, magnetic sensors and cameras collect electromagnetic information to derive accurate, large-scale information about the Earth's surface and atmosphere. Because these data and images are digital, they can easily be quantified and manipulated using computers. RS can be used in efforts to reduce the risk and minimize damage. The same data can be analyzed in different ways for different applications. A number of studies were aiming at identification of crop, area estimation, disease and pest identification, etc. using satellite data in horticulture. The potential use of RS techniques in Horticulture is briefly reviewed in order to exploit the available techniques for efficient crop management.
Bin, Li (author), Shahzad, Muhammad (author), Khan, Hira (author), Bashir, Muhammad Mehran (author), Ullah, Arif (author), and Siddique, Muhammad (author)
Format:
Journal article
Publication Date:
2023-09-18
Published:
Switzerland: MDPI
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 206 Document Number: D12959
20 pages, Sustainable agriculture is a pivotal driver of a nation’s economic growth, especially considering the challenge of providing food for the world’s expanding population. Agriculture remains a cornerstone of many nations’ economies, so the need for intelligent, sustainable farming practices has never been greater. Agricultural industries worldwide require sophisticated systems that empower farmers to manage their crops efficiently, reduce water wastage, and optimize yield quality. Yearly, substantial crop losses occur due to unpredictable environmental changes, with improper irrigation practices being a leading cause. In this paper, we introduce an innovative irrigation time control system for smart farming. This system leverages fuzzy logic to regulate the timing of irrigation in cotton crop fields, effectively curbing water wastage while ensuring that crops receive neither too little nor too much water. Additionally, our system addresses a common agricultural challenge: whitefly infestations. Users can adjust climatic parameters, such as temperature and humidity, through our system, which minimizes both whitefly populations and water consumption. We have developed a portable measurement technology that includes air humidity sensors, temperature sensors, and rain sensors. These sensors interface with an Arduino platform, allowing real-time climate data collection. This collected climate data is then sent to the fuzzy logic control system, which dynamically adjusts irrigation timing in response to changing environmental conditions. Our system incorporates an algorithm that generates highly effective (IF-THEN) fuzzy logic rules, significantly improving irrigation efficiency by reducing overall irrigation duration. By automating the irrigation process and precisely delivering the right amount of water, our system eliminates the need for human intervention, rendering the agricultural system more dependable in achieving successful crop yields. Water supply commences when the environmental conditions reach specific thresholds and halts when the requisite climate conditions are met, maintaining an optimal environment for crop growth.
22 pages, The objective of this study was to provide a comprehensive overview of the recent advancements in the use of deep learning (DL) in the agricultural sector. The author conducted a review of studies published between 2016 and 2022 to highlight the various applications of DL in agriculture, which include counting fruits, managing water, crop management, soil management, weed detection, seed classification, yield prediction, disease detection, and harvesting. The author found that DL’s ability to learn from large datasets has great promise for the transformation of the agriculture industry, but there are challenges, such as the difficulty of compiling datasets, the cost of computational power, and the shortage of DL experts. The author aimed to address these challenges by presenting his survey as a resource for future research and development regarding the use of DL in agriculture.
12 pages, The research aims to identify level of farmers knowledge in Rabia Sub-district/Nineveh governorate on potato cultivation , identify the differences in Knowledge level according to some variables: Age, Education level, years' number of experience potato farming, land ownership, number of dependent information sources of potato , monthly income, type farming career and previous training, and identify the problems facing the potato cultivation. The data were collected by a questionnaire, and analyzed by using Kruskal-Wallis test, Mann_Whitney test. The important results showed that (45.88 %) of the farmers have medium knowledge, the higher knowledge level in crop service field , there are significant differences in knowledge levels according to age, educational level, number of experience years of potato crop, type of farming career and the important problems facing potato farmers is high price of production cost, also there are some recommendations and suggestions.
