فهرست مطالب سینا صمدی قره ورن

Agricultural production involves a series of tasks including tillage, planting, and harvesting, which must be done at the right time for each region and type of product. Failing to complete these tasks on time can lead to a decrease in yield. Farmers may wrongly attribute this to factors such as infertile land, pests, diseases, and uneven rainfall distribution. However, this decrease in yield may not always be evident or tangible. To avoid such losses and unforeseen expenses, it is crucial to plan agricultural mechanization projects using the principles of project control. Agricultural projects, like industrial projects, must be carried out in the correct order and at the right time to achieve optimal results. Given the limited availability of resources for mechanization projects, it is imperative to meticulously plan activities to ensure that they are carried out on time and with maximum utilization of resources. To address these challenges, researchers have used meta-heuristic methods in project control, such as the colonial competition algorithm, which has been proven effective in solving the issue of scheduling projects with limited resources. The algorithm has been tested across various industrial activities and projects, and its performance in scheduling the Resource-Constrained Project Scheduling Problem (RCPSP) has been validated by researchers globally.

There is a scheduling issue regarding limited resources in agriculture, and this study presents a novel approach using the imperialist competitive algorithm (ICA). The algorithm not only explores a wider solution space but also strives to minimize deviation from the optimal solution, thereby improving the success rate of the proposed method. This research focuses on two dominant products, wheat and rapeseed, produced in Moghan Agriculture and Industry located in Northwest Iran. To evaluate the effectiveness of ICA, we compared it with other well-known meta-heuristic algorithms. We successfully resolved the problem of project scheduling problem with limited resources by implementing the imperialist competitive algorithm. Our findings have shown that this approach not only significantly increased efficiency but also outperformed other algorithms.

In this study, we assessed the efficiency of meta-heuristic methods in solving the RCPSP, which can be useful in optimizing the timeliness of project execution, especially for large-scale projects. Some meta-heuristic methods are only useful for smaller problems, while others can provide near-optimal solutions for larger problems, making them suitable for RCPSP. The algorithm explores a wide range of solutions and avoids premature convergence and getting stuck in local optima, unlike other algorithms such as the genetic algorithm. Optimization reduced the required budget and shortened the duration by 42 days for wheat and 25 days for rapeseed.

We utilized the colonial competition algorithm to address the RCPSP problem in agricultural mechanization projects for two agricultural products in Moghan. Our results show that the proposed algorithm converged and reached the optimal solution. The proposed algorithm was compared with other algorithms and it outperformed them.

In many countries, on average, more than 50% of people's food comes from grains, and nearly 70% of the cultivated area of one billion hectares of the world is dedicated to grains. A variety of weeds grow along with cereals in the fields, which can reduce crop yield due to competition for light, water and nutrients. To eliminate weeds accurately and with minimal problems, timely detection with high accuracy and speed is required. be done.
In the field of agriculture, it is controlling and eliminating weeds in grain fields. Weeds are one of the most important factors affecting the production of agricultural products, which are their most important competitors in conventional agriculture, they spray the entire field to eliminate weeds, while weeds appear scattered and patchy in the field. which shows the necessity of using precise agriculture to solve this type of heterogeneity. In addition to causing economic damage, the conventional method of fighting can cause pollution of the environment and even the human food chain. Research shows that the losses caused by pests, diseases and weeds can reach 40% of the global crop every year and it is predicted that this percentage will increase significantly in the coming years. Besides, according to the research of Goktoan et al., the annual cost of weeds for The Australian economy is estimated to be around $4 billion as a loss in agricultural income.

Among the new methods in this field is the use of machine vision technology and related methods such as deep learning object detection algorithms and convolutional neural networks (CNN). The steps related to the implementation of the project include preparing data for training and evaluating networks, using new object detection algorithms, using different convolutional neural networks with different characteristics to extract image features in algorithms, and using the Feature Pyramid Network (FPN) method in object detection algorithms. Was. The output of the networks was evaluated in terms of the number of detections, the exact location of detection and the time of detection in the field. ViTs is based on the Transformer architecture that was originally developed for NLP tasks. Transformers use self-awareness mechanisms that allow the model to capture complex relationships between elements in a sequence. In the case of ViTs, sequence elements are image patches. In using the transformer architecture for visual data, it is dividing the image into small and non-interfering parts. Each patch typically consists of a grid of pixels. These patches are considered the "words" of the image sequence. Spatial embeddings are added to image patches to provide spatial information to the model. Spatial embeddings are necessary because transformers do not have built-in notions of order or spatial relationships. ViTs use multi-series self-awareness mechanisms to capture relationships between different image patches, and the representation of each patch is updated by attention to other patches. Data separation is very important in data watch transformers for two reasons a) the model needs data to learn and b) we need data to measure the model because the model may not be able to extract the information correctly.

The best network in terms of positioning accuracy was the transform model (ViTs) with an average accuracy of 0.95. In addition to this, the network considered in this research managed to recognize 503 of the 535 target weeds, and this means that our network is able to recognize 95% of these weeds. The presented method has been able to reach the highest accuracy compared to other existing methods and has been able to detect existing grasses in a much shorter period of time. Compared to other methods, the reset50 algorithm has been able to detect more than 88%, although its execution time is about 2.5 times that of the proposed method.
In comparing the efficiency of algorithms, execution time is as important as accuracy. By making comparisons and considering 70% of the data as training data and 30% as test data, the presented algorithm has been able to detect the weeds in the field with an accuracy of over 90% in just 13 seconds.

Today, deep learning methods are much more efficient than other methods, so we can use the new methods available in deep learning in the field of agriculture.

Hub Energy systems meet the needs of customers in critical situations by coordinating the use of different infrastructures, resulting in improved system resilience, reduced losses, and reduced operational costs. Due to the increase in the construction of energy hub systems in distribution networks, the issue of the optimal design of energy hubs has received much attention. Considering this issue, this paper presents a new two-stage optimization model to improve the resilience of distribution systems against storms. We formulate the proposed framework as a two-level planning process, taking readiness decisions in the first stage and optimizing operational variables in the second stage to enhance system resilience. We investigate the mutual interactions between electricity and thermal energy using an integrated electricity and gas model, and propose a computational method to model the worst-case scenarios of gas and electricity flow on an integrated IEEE 69-bus network.

Scheduling projects in agriculture include operations and activities that are carried out in a certain order and within a certain period of time. If these operations and activities are not performed on time, due to the quantitative and qualitative decline of the product, a heavy decrease in the income of the farmer or agricultural unit will occur, and these costs are invisible. On the other hand, agricultural operations require the use of resources for implementation, which are usually limited, and if the activities are not optimally allocated, the possibility of the activities not being on time increases. In order to reduce the costs of not being on time, these projects require planning, scheduling and scientific and logical management of time and resources. This has become a standard problem in the field of project scheduling, which has attracted many researchers and they have used different scheduling methods, including exact methods and exploratory and meta-exploratory methods. As a result, various methods of project scheduling problem with limited primary resources have been developed. This article is an overview of research methods and analysis of existing methods that have been published so far. In this article, the goals and approaches to solving the project scheduling problem have been investigated, taking into account some existing works in the field of agriculture, as well as related data.