# A Smart and Mechanized Agricultural Application: From Cultivation to Harvest
## 1 Introduction
Part of these efforts should be the aim of ensuring the full physical, mental, and social well-being of agricultural workers, by minimizing recently increasing work accidents and occupational diseases of those working in the agricultural sector with preventive and protective measures. All these concerns can be resolved in a sustainable way by providing a single mechanism system from planting to harvesting. This study focuses on tracking and harvesting crops on large-scale farmlands in three stages. In the first phase (_cultivation_), planting the crops in the most optimized way is sought by using an algorithmic approach (Swarm Sand Cat Optimization (SCSO) algorithm) during the planting phase. In this way, the location of the crops that need to be planted (without the need for human or manual intervention) is determined in accordance with the specific requirements of any crops. An optimization approach can be a good solution since it is a nondeterministic polynomial-time hardness (NP-Hard) problem to meet this need with analytical and similar solutions. The SCSO algorithm could be a successful solution in determining the correct places of the products in accordance with its working mechanism. In this way, optimized results can be obtained without human intervention, with the lowest error rate and lower operation cost. In the second stage (_control and monitoring_), the growing processes of the planted crops are tracked and monitored thanks to IoT devices. With the IoT devices, the status of the planted products is constantly checked, and the results are transferred to the next stage as an input for instant processing. The third phase (_harvesting_) is the product collection stage. At this stage, a metaheuristic method (Reverse Ant Colony Optimization) is proposed. The results from the third stage are presented as the optimum path for autonomous robots (IoVs) to harvest crops. In this third phase, a new fitness function is also defined. Since the second and third stages are carried out simultaneously, it is ensured that there will be a sustainable model. The Reverse ACO (RACO) is proposed as a new improvement to the ACO algorithm. Thanks to this algorithm, the results from the second stage can consistently and regularly provide the input for the third stage correctly. The performance of the proposed method was evaluated in different scenarios and situations and compared with similar studies in the literature. The following benefits were also obtained with the application developed in this study:  
1. The efficient use of resources such as human and natural resources by providing sustainable and smart agriculture. In other words, it allows farmers to constantly monitor crop variability and stress conditions. In addition, the occupational health and safety of the farmers can be increased, the best products can be harvested, and efficient resource consumption and profit increase will be possible. 2. Assistance in gaining maximum profit with minimum consumed energy and time by considering several objectives in the fitness function. As an example, the proposed mechanism generates the optimized routes for each autonomous robot in harvesting crops at the annual harvest. 3. Providing decision-makers with an infrastructure architecture for autonomous agricultural robots. 4. Collecting crops in the required periods with a scheduling mechanism; thus, it prevents early or late harvesting. Meanwhile, it is adaptive to several crop types. In this study, tomatoes were considered as an example case. 5. Maximum profit, maximum quality, efficient use of resources such as human labor and water, the accurate location for planting each crop, finding the best time to harvest, and consuming the least power. In Section 2 of this paper, the literature review is discussed. The proposed model and application are explained in Section 3. In Section 4, the simulation results are evaluated. The last section of the study includes the conclusions and possible further studies.