The excessive consumption of herbicides has gradually led to the herbicide resistance weed phenomenon. Managing herbicide resistance weeds can only be explicated by applying high-tech strategies such as artificial intelligence (AI)-based methods. We review here AI-based methods and tools against herbicide-resistant weeds. There are a few commercially available AI-based tools and technologies for controlling weed, as machine learning makes the classification process significantly easy, namely remote sensing, robotics, and spectral analysis. Although AI-based techniques make outstanding improvements against herbicide resistance weeds, there are still limited applications compared to the real potential of the methods due to the challenges. In this review, we identify the need for AI-based weed management against herbicide resistance, comparative evaluation of chemical vs. non-chemical management, advances in remote sensing, and AI technology for weed identification, mapping, and management. We anticipate the ideas will contribute as a forum for establishing and adopting proven AI-based technologies in controlling more weed species across the world.

Annual cane pruning of grape vineyards is a time-consuming and labor-intensive job, but no mechanized or automatic way has been developed to do it yet. Robotic pruning can be a perfect alternative to human labor. This article proposes a systematic seven-stage procedure to design a kinematically optimized manipulator, named ‘Prubot’, to manage vineyards’ cane pruning. The manipulator structure was chosen, resulting in a 7R (Revolute) manipulator with a spherical shoulder and wrist. To obtain the design constraints, the manipulator task space was modeled. The robot’s second and third link lengths were determined by optimizing the global translational version of the measure of manipulability and the measure of isotropy of the manipulator arm section. Finally, simulations confirmed the appropriateness of the manipulator workspace. Furthermore, sampling-based path planning simulations were carried out to evaluate the manipulator’s kinematic performance. Results illustrated the impressive kinematic performance of the robot in terms of path planning success rate ($\cong 100\%$). The simulations also suggest that among the eight single-query sampling-based path planning algorithms used in the simulations, Lazy RRT and KPIECE are the best ($\le 5 \mathrm{s} \& ~100\%$) and worst $\left(\ge 5 \mathrm{s} \&\le 25\%\right)$ path planning algorithms for such a robot in terms of computation time and success rate, respectively. The procedure proposed in this paper offers a foundation for the kinematic and task-based design of a cane pruning manipulator. It could be promisingly used for designing similar agricultural manipulators.

Huanglongbing (HLB or citrus greening) is a disease caused by an insect-transmitted bacterial pathogen Candidatus Liberibacter asiaticus (CLas). Thermotherapy has been successfully used by others to reduce the population of CLas bacteria in HLB-affected citrus trees under greenhouse studies. Thermotherapy is the application of heat as a strategy to reduce the adverse economic impact of certain pests and diseases. CLas is a fastidious, non-cultivable organism. The high variance in CLas titers in canopy samples together with this lack of cultivability makes it impossible to use classical bacteriological techniques to measure the viability either before or after treatments. Therefore, we used the survival rates of a surrogate bacterium, Klebsiella oxytoca, in order to evaluate the effectiveness of a mobile thermotherapy delivery system developed for in-field treatment of HLB-affected trees. K. oxytoca is a Gram-negative, rod-shaped bacterium that was originally isolated from soil and has been used in the development of industrial applications related to ethanol fuel production. It served as a biologically-based sensor of temperature stress (biosensor) in this study. Thermocouples and biosensor packets (plastic cups with suspended small snap-top tubes) containing the K. oxytoca were attached to an HLB-affected citrus tree and their canopy locations mapped. The mobile thermotherapy treatment hood covered the canopy of the HLB-affected tree. Then, steam and hot water were injected through nozzles inside of the hood to increase the temperature of the tree canopy. A standard temperature–time combination of 54 °C for 90 s was chosen based on preliminary studies where heat treatment parameters caused a significant reduction in CLas populations without inflicting permanent damage to the tree. The survival ratio of the K. oxytoca in the biosensor packets was found to range from complete elimination to 5% with treatments of 250 s and a maximum temperature of 54 °C.

Heat treatment has been applied in previous studies to treat diseased plants and trees affected by heat-sensitive pathogens. Huanglongbing (HLB) is a heat-sensitive pathogen and the optimal temperature–time for treating HLB-affected citrus trees was estimated to be 54 °C for 60 to 120 s from indoor experimental studies. However, utilizing this method in orchards is difficult due to technical difficulties to effectively apply heat. Recently, a mobile thermotherapy system (MTS) was developed to in-field treat HLB-affected trees. This mobile device includes a canopy cover that covers the diseased tree and a system to supply steam under the cover to treat the tree. It was proven that the temperature inside the canopy cover can reach the desired one (i.e., 54 °C) to kill bacteria. However, for HLB, the heat should penetrate the tree’s phloem where the bacteria live. Therefore, measuring the heat penetration inside the tree is very critical to evaluate the performance of the MTS. In this study, a heat transfer model was developed to simulate the heat penetration inside the tree and predict the temperature in the phloem of the diseased tree during the in-field heat treatment. The simulation results were compared with in-field experimental measurements. The heat transfer model was developed by a comparative analysis of the experimental data using the ANSYS software. Results showed that the temperature in the phloem was 10–40% lower than the temperature near the surface of the bark. Simulation results were consistent with experimental results, with an average relative error of less than 5%.

This study addresses the escalating global concern surrounding land degradation (LD) and its far-reaching implications on water and nutrient availability, as well as on human health and well-being. Focused on the southeastern Caspian Sea region, this research employs a novel remote sensing geo-pedological methodology to comprehensively assess soil and land quality dynamics, particularly influenced by salts, and investigates the intricate relationship between LD and soil development. The study area, marked by a susceptibility to seawater level fluctuations and diverse landforms (lagoons, barriers, and coastal plains) offers a unique opportunity for geopedologic analysis. Utilizing particle size distribution data, six distinct landforms are identified, providing insights into the region’s complex sedimentary history. A soil quality assessment is conducted remotely through the calculation of two indexes—the Integrated Quality Index (IQI) and the Nemoro Quality Index (NQI)—employing both Total Data Set (TDS) and Minimum Data Set (MDS) methodologies. The investigation highlights the role of soluble salts in shaping soil quality, thereby influencing LD and development dynamics. The differentiation of landforms significantly enhances classification accuracy, providing a more nuanced understanding of the multifaceted factors governing LD. The study’s implications extend beyond the southeastern Caspian Sea region, and demonstrate that the potential for incorporating a geopedologic approach when assessing soil and land quality dynamics in arid regions globally. Our analytic approach can inform policymakers and land managers when making decisions to combat LD and foster sustainable land development. This research also contributes towards advancing knowledge in geopedology by providing a robust foundation for future studies aimed at enhancing land management practices in the face of ongoing environmental challenges.