Soil moisture or moisture content is a fundamental constituent of the hydrological system of the Earth and its ecological systems, playing a pivotal role in the productivity of agricultural produce, climate modeling, and water resource management. This review comprehensively examines conventional and advanced approaches for estimation or measuring of soil moisture, including in situ methods, remote sensing technologies, UAV-based monitoring, and machine learning-driven models. Emphasis is primarily on the evolution of soil moisture measurement from destructive gravimetric techniques to non-invasive, high-resolution sensing systems. The paper emphasizes how machine learning modules like Random Forest models, support vector machines, and AI-based neural networks are becoming more and more popular for modeling intricate soil moisture dynamics with data from several sources. A bibliometric analysis further underscores the research trends and identifies key contributors, regions, and technologies in this domain. The findings advocate for the integration of physics-based understanding, sensor technologies, and data-driven approaches to enhance prediction accuracy, spatiotemporal coverage, and decision-making capabilities.

The application of pesticides is one of the important aspects of a crop production system. The development of a single spraying system for all types of pesticide application is a solution for a cost-effective and efficient crop production system. Standardizing the droplet sizes at different operating pressure of hollow cone nozzle will be a solution for controlling the weeds as well as the other pests. A small tractor-operated hollow cone-based boom sprayer was developed to overcome said problems. The developed sprayer can be mounted on three-point linkage and can be operated by tractor PTO. The developed system was evaluated for 0.1, 0.2 and 0.3 MPa of operating pressure through water-sensitive papers and ImageJ software. Under this pressure, the selected hollow cone nozzle produced medium to coarser droplet size. The highest VMD of 346.4 urn was obtained under operating pressure of 0.1 MPa and the least VMD of 277.1 um was obtained under operating pressure of 0.3 MPa. The increase in operating pressure causes a reduction in droplet size. However, the relative span (RS) was increasing with a decrease in operating pressure. It was 0.72 at an operating pressure of 0.3 MPa and increased to 1.27 at an operating pressure of 0.1 MPa. The highest weed control efficiency (WCE) of 88.1% was obtained under 0.1 MPa of operating pressure. However, the operating pressure does not had significant effect on WCE.

Specific energy prediction is critically important to enhance field performance of agricultural implements. It enables optimal utilization of tractor power, reduced inefficiencies, and identification of comprehensive inputs for designing energy-efficient implements. In this study, A 3-5-1 artificial neural network (ANN) model was developed to estimate specific energy requirement of a vertical axis rotor type intra-row weeding tool. The depth of operation in soil bed, soil cone index, and forward/implement speed ratio (u/v) were selected as the input variables. Soil bin investigations were conducted using the vertical axis rotor (R_VA), interfaced with draft, torque, speed sensors, and data acquisition system to record dynamic forces employed during soil–tool interaction at ranges of different operating parameters. The depth of operation (DO) had the maximum influence on the specific energy requirement of the R_VA, followed by the cone index (CI) and the u/v ratio. The developed ANN model was able to predict the specific energy requirements of R_VA at high accuracies as indicated by high R² (0.91), low RMSE (0.0197) and low MAE (0.0479). Findings highlight the potential of the ANN as an efficient technique for modeling soil–tool interactions under specific experimental conditions. Such estimations will eventually optimize and enhance the performance efficiency of agricultural implements in the field.

Traditional manual weed management is one of the tedious and costly operations in the complete cycle of crop production, reasons being high labor costs, time and tedium. The herbicide use contributes to environmental pollution in addition to other disadvantages of concern. The increasing demand for toxicant free food has become a challenge for weed control. Hence, the mechanical weeding is gaining importance. Automation in agriculture has also improved the mechanization input in weed management. The rapid entry of sensors, microcontroller and computing technologies in the field has formed a foundation of agricultural autonomous guidance systems. An automated system is time effective for field operations, avoids huge labor requirement and health drudgery issues to provide an efficient farm operation. Generally manual tools such as khurpi (hand operated small hoe), grubber, spade, wheel hoe, push pull type of weeder are used by farmers for the removal of inter- and intra-row weeds with higher weeding efficiency in the range of 72 to 99% but field capacity is very low in the range of 0.001 to 0.033 ha/h. This review deliberates on the latest work being done on mechanical weed management such as tractor operated finger weeder, torsion weeder, ECO weeder, flame weeder, harrow and sensor-based technologies for management of inter- and intra-row weeds in crops with wider rows.

Conventional methods of nano-urea application in maize cultivation, such as tractor-operated gun sprayers, involve high water usage, labor intensity, and operator health risks due to chemical exposure. The drone spraying system ensures precise and automated application of nano-urea with minimal resource use, labor requirement, and operator intervention. However, the efficacy of the drone spraying system for nano-urea application was not evaluated and compared with traditional spraying systems in field conditions. There is a need to evaluate whether drone-based spraying systems can provide an equally effective and more resource-efficient alternative to conventional spraying techniques. Therefore, this study evaluated the agronomic efficacy of a drone-based spraying platform in comparison to conventional tractor-operated gun sprayers for the foliar spray application of nano-urea in the maize crop. Field experiments were conducted during the 2024 Kharif season to evaluate changes in SPAD, NDVI values, and grain yield due to two spray application methods. Both spraying methods showed statistically similar NDVI and SPAD values eight days after nano-urea application, indicating comparable effectiveness in nutrient delivery. Maize yield was also observed to be statistically indistinguishable between the two methods (t (8) = 0.025503, p = 0.9803), with 2912 ± 375 kg/ha (mean ± SE) for the gun sprayer and 2928 ± 503 kg/ha for the drone sprayer treatments. However, the drone system demonstrated significant operational advantages, including 95% water savings and decreased operational time. These findings support the use of drone spraying as a sustainable, safe, and scalable alternative to traditional fertilization application practices in precision agriculture.