Corn is a vital global crop, yet its cultivation demands extensive agrochemical inputs, prompting the need for sustainable alternatives. This study investigates the impact of vermicompost (VC) and vermicompost tea (VCT) applications on corn growth, physiology, and resistance to Fall Armyworm (FAW) infestation using advanced optical plant sensors. Six treatments were employed: V0 (control), VC1, VCT100, VC1 + VCT50, VC3, and VC3 + VCT50. During the growing season, plant growth parameters, such as height, chlorophyll content, and spectral reflectance were measured using a chlorophyll meter, fluorometer, porometer, and spectroradiometer. Results indicated that VC-treated plants exhibited superior growth and higher chlorophyll content than control or untreated plants. The VC1 + VCT50-treated plants showed robust resistance to FAW, with no infestation throughout the season, while VC1-treated plants showed delayed attack by FAW. Soil chemical analysis showed that VC and VCT treatments had similar nutrient concentrations as the control. Plant nutrient content was higher in VCT100 compared to all treatments. These findings suggest that the combined application of VC and VCT, particularly at specific application rates, can enhance corn plant health, mitigate pest damage, and optimize yield potential.

Recent studies on hyperspectral remote sensing (HSR) have shown that the estimation accuracy of different vegetation characteristics improves when the HSR data are corrected for the bidirectional reflectance distribution function (BRDF) effects. Similar studies involving soil parameters are limited. Here, we used the BRDF-corrected HSR data collected using the airborne visible-infrared imaging spectrometer-next generation (AVIRIS-NG) sensor to estimate soil parameters over a 138-km(2) agricultural catchment. Surface soil samples were collected from 173 ground reference locations (GRLs) from this catchment to measure clay and sand contents, pH, electrical conductivity (EC), and soil organic carbon (SOC) contents. The BRDF correction was applied using the flexible BRDF (FlexBRDF) algorithm, and a polynomial unmixing approach was used to extract soil spectra from the corrected image. The BRDF correction successfully removed the shading effects and produced smooth transitions along the overlapping regions when multiple AVIRIS-NG images were mosaicked. Upon unmixing, soil spectra could be extracted at 140 GRLs when BRDF-corrected spectra were used, while uncorrected spectra produced soil spectra only for 114 GRLs. Chemometric models were validated using 109 common GRLs to compare estimation accuracy across laboratory-measured soil spectra (SSLab) and those obtained from unmixing of BRDF-corrected and uncorrected spectra. The coefficient of determination (R-2) values in the validation datasets ranged from 0.40 to 0.83 for both the BRDF-corrected and SSLab data, while the uncorrected spectra showed poor estimation accuracy (R-2: 0.25-0.56). The resulting root-mean-squared error (RMSE) was reduced by 10% and 47% for the BRDF-corrected soil spectra compared to their uncorrected data. The BRDF-corrected and unmixed soil spectra were used to map soil properties at similar to 5-m spatial resolution for the entire catchment. Low SOC contents in the resulting maps adjoining the Ganges river flowing through our study site captured the topsoil loss typically observed from river banks. Thus, the BRDF-corrected HSR data not only improved the accuracy of soil estimates but also showed potential to identify vulnerable areas needing precision management measures with high spatial resolution.

The success of weed control is critical for our food security. Non-chemical weed control is a promising technique in sustainable agriculture to ensure the food security. In this review, multiple directed energy weed control methods are reviewed with a specific focus on laser and optical radiation weed control. The mechanisms of the weed control in terms of adverse ablation, radiation thermal effects, and molecular-level damages are systematically reviewed. In particular, the underlying mathematical models determining the dose and response relationship of the weed control are also analyzed for a rigorous study of the physical law of the control process. Challenges of applying the techniques into practice are also illustrated to guide practical weed control applications.

Commercial agricultural production of orchards is based on water and fertilizer applications. Over-application and orchard spatial variability lead to water and nitrogen (N) losses through leaching and environmental damage. This work introduces a novel advanced method to quantify N and water leaching at the orchard scale by combining soil monitoring, unmanned aerial vehicle (UAV) imagery to estimate tree size, leaf N and canopy content, and water flow and N transport models. Four neighboring commercial orange orchards were selected, and 48 representative trees were instrumented with suction cups and tensiometers at a depth of 90 cm. The physical properties of the soil profiles in the vicinity of each tree were determined. During 2019-2021, UAV structure-from-motion (SfM) photogrammetry was used to classify the tree sizes into small, medium, and big categories. Soil porewater extraction and soil matric potential measurements were conducted every three weeks, while leaf N contents (LNC) were determined through bimonthly leaf tissue sampling. The LNC, N leaching, soil water state, and N use efficiency (NUE) (the ratio between the mass of plant N uptake and the mass of N applied per area) were strongly related to tree size classification. Results of the calibrated hydrological model illustrated that the 'big' trees exhibited minimal N leaching due to higher transpiration compared to the other tree size categories. An NUE map was established using the calibrated model and field measurements, demonstrating that soil spatial variability minimally affected N leaching compared to tree size distribution. The presented holistic approach can be used to identify N-leaching hotspots, improve orchard scale NUE estimates, and promote sustainable agricultural management practices.

Weed harrowing is commonly used to manage weeds in organic farming but is also applied in conventional farming to replace herbicides. Due to its whole-field application, weed harrowing after crop emergence has relatively poor selectivity and may cause crop damage. Weediness generally varies within a field. Therefore, there is a potential to improve the selectivity and consider the within-field variation in weediness. This paper describes a decision model for precision post-emergence weed harrowing in cereals based on experimental data in spring barley and nonlinear regression analysis. The model predicts the optimal weed harrowing intensity in terms of the tine angle of the harrow for a given weediness (in terms of percentage weed cover), a given draft force of tines, and the biological weed damage threshold (in terms of percentage weed cover). Weed cover was measured with near-ground RGB images analyzed with a machine vision algorithm based on deep learning techniques. The draft force of tines was estimated with an electronic load cell. The proposed model is the first that uses a weed damage threshold in addition to site-specific values of weed cover and soil hardness to predict the site-specific optimal weed harrow tine angle. Future field trials should validate the suggested model.

Precision agriculture (PA), also known as smart farming, has emerged as an innovative solution to address contemporary challenges in agricultural sustainability. A particular sector within PA, precision viticulture (PV), is specifically tailored for vineyards. The advent of the Internet of Things (IoT) has facilitated the acquisition of higher resolution meteorological and soil data obtained through in situ sensing. The integration of machine learning (ML) with IoT-enabled farm machinery stands at the forefront of the forthcoming agricultural revolution. These data allow ML-based forecasting as an alternative to conventional approaches, providing agronomists with predictive tools essential for improved land productivity and crop quality. This study conducts a thorough examination of vineyards with a specific focus on three key aspects of PV: mitigating frost damage, analyzing soil moisture levels, and addressing grapevine diseases. In this context, several ML-based models are proposed in a real-world scenario involving a vineyard located in Southern Italy. The test results affirm the feasibility and efficacy of the ML models, demonstrating their potential to revolutionize vineyard management and contribute to sustainable agricultural practices.