learning models for weed detection in smart agriculture [5] and the impact of microbial preparations on yields and soil nutrients [6] are discussed. In addition, it examines the energy consumption and environmental impact of lettuce production [7] and the resistance to airflow in aerated wheat masses [8]. Finally, it investigates real-time plant classification using deep learning techniques [9] and the design of a controller for pH regulation of liquid fertilizers [10]. It includes research on the analysis and validation of a foreign material shaking machine for pepper harvesters [11], the effect of pulsed electric field conditioning on leaf-wood burning characteristics [12], and the detection and factors that induce _Stenocarpella_ spp. survival in maize stubble and soil suppression [13]. Moreover, this monograph investigates the adoption and use of mobile internet technology in sustainable agriculture among wheat farmers [14], compression and heat generation by fungi in bulk maize with consideration of kernel cracking [15], and the implementation of a smart irrigation system with consideration of optimal energy management [16]. In addition, a method for extending the shelf life of apples after storage using low magnetic fields [17] and the theory of motion of a machine-tractor-trailer combination for harvesting beet tops [18] have been investigated. These studies provide valuable insights into innovative technologies, management practices, and theoretical frameworks that contribute to the development of agricultural practices, crop quality, and resource efficiency. One of the areas of interest in this monograph is the control of strawberry wilt disease caused by _Fusarium solani_, which is an economic challenge for strawberry producers in Mexico [19]. The in vitro efficacy of four fungicides against _F. solani_ is being evaluated to identify effective means of controlling this devastating disease [19]. Similarly, the use of autonomous drones equipped with deep learning algorithms to detect _Spodoptera frugiperda_, a destructive pest of maize, based on foraging symptoms observed in RGB images, is being investigated [20]. This application of digital technologies has the potential to revolutionize pest detection in precision agriculture. The use of image processing and deep learning algorithms to identify root rot disease in Korean ginseng plants from RGB plant images has been proposed [21]. An inexpensive method for assessing the positioning accuracy of a global navigation satellite system was presented, providing a flexible framework for assessing the precision of agricultural machinery [22]. The efficiency of selective fertilization of leafy vegetables with manure was also investigated [23]. Other research has developed a device to measure the ground pressure of paddy fields using internet of things technology and wireless data transmission [24]. In addition, the effect of pulsed magnetic fields on the cation profile of sunflower, cress, and radish sprouts was investigated, revealing potential benefits for seed germination and nutrient content [25]. This monograph also discusses advances in agricultural machinery. Image recognition algorithms have been developed and compared, which play a key role in effectively distinguishing between different parts of the sweet pepper plant for automation purposes. Using techniques such as normalized difference vegetation index (NDVI) and local feature analysis, this study compares the performance of different algorithms. The results demonstrate the capabilities of these algorithms, with significant successes achieved by the convolutional neural network (CNN) approach [26]. An architecture potentially capable of optimizing productivity has been proposed, as it uses simulation software to optimize (i) climate control strategies related to crop microclimate control and (ii) crop management treatments [27].