Subhaprada Dash

Background: Traditional weed management in rice farming relies heavily on herbicides and labor-intensive manual practices, which are often costly, inefficient and environmentally unsustainable. The lack of precision in conventional approaches results in excessive chemical use, crop damage and inconsistent weed control. To address these challenges, intelligent robotic systems integrated with artificial intelligence offer a promising pathway toward sustainable and precise weed management. Methods: This study presents a comprehensive analysis of a modular autonomous robotic platform that integrates advanced mechatronic design with AI-driven visual intelligence for rice-field weed management. Three deep learning models, YOLO v5, v7 and v8, were trained on rice weed image datasets for real-time weed detection and classification. Advanced image-processing algorithms were employed for crop-weed discrimination and row guidance. The robotic platform, powered by an NVIDIA Jetson Nano and planetary gear motors, features a dual-action mechanical subsystem consisting of a rotary weed cutter for physical removal and a cultivator–incorporator for in-situ biomass mixing. Field experiments were conducted at the Agricultural Farm of Siksha ‘O’ Anusandhan (SOA) University, Odisha, India. Result: The robotic platform demonstrated stable real-time navigation, achieving a lateral tracking deviation of approximately ±2 cm under controlled test conditions and less than 5 cm in field environments, as quantified by measuring the robot’s offset from the crop-row centreline at fixed reference intervals. Field trials demonstrated approximately 95% weed control efficiency and less than 2% crop damage. Compared with conventional practices, the robotic system reduced herbicide use by nearly 70% while maintaining stable operation under representative paddy-field conditions. Detection accuracy and field-level weed-removal efficiency are distinct evaluation metrics. The proposed AI-integrated robotic platform demonstrates strong potential for precision weed management in rice cultivation. By combining deep-learning-based vision with a robust mechatronic framework, the system significantly reduces chemical dependency, improves weed-control accuracy and enhances environmental sustainability. This work highlights a viable pathway to scalable, eco-friendly automation in paddy-field agriculture.

Aerobic rice systems require efficient nutrient and weed management to sustain productivity, yet complex nitrogen dynamics and severe weed competition often reduce growth and yield. Scientific evidence on the combined effects of integrated nitrogen sources and weed management strategies in aerobic rice remains limited. A two-year split-plot field experiment was conducted during Kharif 2023 and 2024 to investigate the effect of integrated nitrogen and weed management practices on crop growth, soil microbial attributes, and weed density in aerobic rice. The experiment comprised of four levels of nutrient management treatments in main plot, (N1 - 100% STBNR (Soil Test Based Nitrogen Recommendation), N2 - 75% STBNR + 25% nitrogen supplied through FYM (Farm Yard Manure), N3 - 75% STBNR + Soil application of Azotobacter and Azospirillum, and N4 - 75% STBNR + 25% nitrogen supplied through FYM + soil application of Azotobacter and Azospirillum) and four sub plot treatments comprised of Pendimethalin (1 kg/ha) applied one day after sowing followed by bispyribac-sodium at 25 DAS; a stale seedbed (15 DBS) followed by brown manuring plus bispyribac-sodium (25 g/ha) at 25 DAS; a stale seedbed (15 DBS) followed by one hand weeding at 25 DAS; and a weedy check replicated thrice. The results revealed significant interaction between nitrogen and weed management across the pooled years. The treatment, combining 75% STBNR + 25% N through FYM + Azotobacter and Azospirillum with SSB fb 1 HW at 25 DAS, recorded the tallest plants (74.2 cm) and the highest LAI (4.19), statistically at par with the same under SSB fb BM. This treatment also exhibited a notably higher bacterial population and microbial biomass, indicating improved soil biology and nutrient availability. The lowest weed density was observed in 75% STBNR +25% nitrogen through FYM with SSB fb BM (7.15 no./m²), which was at par with 75% STBNR +Azotobacter and Azospirillum with SSB fb BM. Plant height, and LAI showed significant positive correlations with bacterial population and microbial biomass carbon and negative correlations with total weed density and weed biomass, whereas weed indices strong positive relationships with weed density and biomass.

Objective: To assess groundwater pollution vulnerability in the Chennai Metropolitan Region using a fuzzy logic-based model integrated with Geographic Information System (GIS) tools, overcoming the limitations of conventional index-based approaches in handling uncertainty and diverse data types. Methods: Seven parameters, depth to water table, net recharge, soil texture, aquifer media, hydraulic conductivity, slope, and land use/land cover, were processed through triangular and trapezoidal fuzzy membership functions. The Mamdani inference method and expert-defined IF–THEN rules generated a continuous Vulnerability Index (VI) from 0 to 100, later classified into five vulnerability zones. The model’s performance was checked using nitrate data from 100 groundwater samples and evaluated with ROC analysis. Sensitivity analysis measured the influence of each parameter. Results: High and very high vulnerability zones, mainly along coasts, floodplains, and urban areas with shallow aquifers, aligned with nitrate hotspots, while inland crystalline regions showed low risk. ROC analysis (AUC ~0.93) showed high prediction accuracy, and sensitivity tests proved the fuzzy logic–GIS model was reliable for mapping groundwater contamination risk. Conclusion: Fuzzy logic–GIS integration is an effective, adaptable approach for groundwater vulnerability mapping in complex hydrogeological settings. The resulting map provides a spatially explicit decision-support tool for targeted land-use control, wastewater management, and nitrate pollution reduction, with applicability to other coastal urban aquifers.

Green gram is an important pulse crop in India. Its cultivation is affected due to several biotic and abiotic factors, which have reduced the yield significantly. Among these factors, weed infestation is a prominent challenge that can lead to significant reductions in productivity. A field experiment was conducted with ten weed management treatments to evaluate the weed growth, yield and economics of green gram during summer, 2022. Ten treatments were evaluated, including techniques such as stale seedbed, pendimethalin at 750 g ha-1, imazethapyr at 75 g ha-1 and hand weeding. The dominant weeds in the experimental area were Digitaria sanguinalis, Poa annua and Melochia corchorifolia. The lowest biomass and density of weeds were achieved with two-hand weeding, which was comparable to treatments involving pendimethalin fb imazethapyr and stale seedbed fb imazethapyr. Among the weed management practices, the lowest value of weed index was recorded in pendimethalin fb imazethapyr (4.95 %), followed by stale seedbed technique fb imazethapyr (7.26 %), whilst it was the highest under weedy check (60.34 %). The maximum seed yield was noted in the hand weeding treatment (10.70 q ha-1), which was followed by pendimethalin fb imazethapyr (10.17 q ha-1) and stale seedbed fb imazethapyr (9.92 q ha-1). Pendimethalin fb imazethapyr also fetched the highest net return ₹ 39335 ha-1) and B:C (2.14) and at par with stale seedbed fb imazethapyr (₹ 37240 ha-1 and 2.07). The study concluded that pendimethalin or stale seedbed with imazethapyr provided effective weed management and enhanced productivity and profitability in summer green gram.

Weed management plays a crucial role in enhancing the productivity and sustainability of finger millet cultivation, particularly under diverse agro-ecological conditions. A field experiment was conducted at Agricultural Research Station, Bhubaneswar, Odisha to study the effect of weed management practices on weed growth, crop growth and productivity of finger millet during the kharif season of 2023. The experiment included eight treatments combining stale seedbed, closer row spacing, bispyribac-Na, two hand weedings, and a weedy check. These were arranged in a randomised block design with three replications. Twice-hand weeding recorded the lowest weed density (14.0 No./m²) and dry weight (8.0 g/m²), comparable to the stale seedbed method followed by closer spacing (21.8 No./m², 12.9 g/m²). The maximum tiller number, dry matter accumulation, and ear count per square meter were achieved with the two-hand weeding treatment performed at 20 and 40 days after sowing (DAS). This was closely followed by the stale seedbed method combined with closer spacing, as well as closer spacing combined with bispyribac-sodium application. In terms of productivity, the highest yield of 2217 kg/ha was recorded under the two-hand weeding treatment. This yield was statistically comparable to the yields obtained from the stale seedbed technique followed by (fb) closer spacing (2060 kg/ha) and closer spacing fb bispyribac-sodium at 15 g/ha at 15 DAS (1884 kg/ha). In terms of grain yield, twice hand weeding was 7.62% higher than stale seedbed technique fb closer spacing. The next best treatment in maximization of yield was the integrated application of stale seedbed fb bispyribac-sodium (1645 kg/ha) treatment in finger millet. Economic analysis of finger millet exhibited the highest return per rupee invested (2.45) under stale seedbed fb closer row spacing. Integrated weed management using the stale seedbed technique 10 days before sowing followed by 15 cm row spacing proved most effective, offering practical benefits for farmers by ensuring better weed control, enhanced crop growth, higher yields, and improved profitability in finger millet during the kharif season.

There is compelling evidence that organic inputs encourage soil health and sustainable agriculture. Nonetheless, it is currently unclear how long-lasting organic additions in combination with chemical fertilizers affect soil macro aggregate production and the soil's ability to store organic carbon (SOC). Therefore, the research goal was to determine how after 14 years of fertilizing, there is interaction between soil carbon sequestration and soil aggregate. Seven interventions were used in a field trial. Farmer-applied nitrogen (N), improved N executives, bio-char and manure, straw, compost, biogas waste, or enhanced N management alone is chosen. The research showed that, increasing by 30.6-120.8% and 11.3-36.3% in the soil layers between 0 -16 cm and 16 - 31 cm, respectively. A indicator of the soil's carbon storage process, human carbon (HUC) was one of the soil's humus carbon pools that grew by 15.7 to 206.2% in the soil depth of 0–16 cm and between its high aromatic carbon concentration and biodegradation resistance, adding bio-char to the organic components was the most successful tactic. The concentrations of dissolved organic matter (DOC), particulate organic carbon (POC), and microbial biomass carbon (MBC) and - the oxidizable carbon (ROC) all increased on average by, 122.5%, 94.3%, 218.2%, and 87.5%, respectively. The amount of exchangeable , , and Fe, with soil Al oxides, the mean weight diameter (MWD),these indicators had a strong association. According to the findings, adding organic materials to calcareous soil could be a useful management technique for encouraging carbon retention and creating soil macro aggregates in the ground.

The Indian Sundarbans, a UNESCO World Heritage Site, is facing severe water management challenges due to climate change, population growth, and economic development. This study analyzes the economic benefits of sustainable water management practices in the Indian Sundarbans. This study examines the economic benefits of sustainable water management practices in the Indian Sundarbans. Using a combination of literature review, case study, and economic analysis, we find that sustainable water management practices can generate significant economic benefits, including increased crop yields, reduced water treatment costs, job creation, and livelihood improvement. Our results show that sustainable water management practices can increase crop yields by up to 30%, reduce water treatment costs by up to 50%, and create up to 10,000 jobs in the region. We also estimate that the ecosystem services provided by sustainable water management practices are valued at up to $1.3 billion annually. Our findings have important implications for policy and decision-making on sustainable water management practices in the Indian Sundarbans. Water protection and management is vital for socioeconomic development and ecological diversity. Groundwater resources are of key importance to sustainable development. While planning for the urban settlement and industries, the occurrence of adequate water resource is the crucial component for the life. The aim of the study is to analyses the rapid industrialization and economic development leading to a tremendous increase in the use of groundwater.

Cultivars and nutrient management determine productivity and quality of the organic finger millet. A field trial was carried out during the kharif season 2022 at Binjhagiri, Bhubaneswar, Odisha comprising the four cultivars of finger millet and four nutrient management practices through organic manure combinations with split plot design replicated thrice. The four cultivars namely, C1- ‘Arjuna’, C2- ‘Bhairabi’, C3- ‘Chilika’ and C4- ‘Kalua’ were assigned to the main plots. Recommended dose of nitrogen (RDN) such as 50 kg ha-1 was supplied through varying percentage of organic manures namely, farmyard manure (FYM), neem oilcake (NOC) and vermicompost (VC). Four nutrient management practices such as N1- FYM (RDN100), N2- FYM (RDN40) + NOC (RDN30) + VC (RDN30), N3- FYM (RDN50) + NOC (RDN25) + VC (RDN25), N4- FYM (RDN60) + NOC (RDN20) + VC (RDN20) were assigned to the sub-plots. Combination of the treatments viz., C3N2, C4N2, C3N3 and C4N3 excelled over the others for grain and protein yields and profitability, but treatment combination C4N2 was the most promising considering the parameters like grain yield (2.10 tha-1), net return (Rs. 36940 ha-1), protein yield (165.75 kg ha-1), calcium yield (7.34 kg ha-1) and iron yield (0.137 kg ha-1). There was significant positive correlation of grain yield with all growth-yield parameters, except test weight, whereas regression analysis indicated 70.86, 85.21 and 73.45% contribution of tillers hill-1, dry matter production and leaf area index at harvest, respectively, to variations in grain yield. The organic finger millet growers should cultivate cv. ‘Kalua’ with application of FYM+NOC+VC @ 4.35+0.34+0.6 t ha-1 for achieving maximum yield and grain quality.

The long-term goal of this study is to look at how pest populations change, how pesticides work against them, and what effects this has on the cost of growing grains. Over the past ten years, climate change, farming practices, and more global trade have all changed the agricultural environment. These changes have led to the appearance of new pests or minor pests becoming major pests. To come up with effective ways to get rid of pests, you need to understand these trends. Our researches show that pest populations have changed a lot, with new pests have come into environment and familiar pesticides are no longer effective against pests. The infestation of fall armyworms in corn increased by 80%, and the number of brown planthoppers that were resistant to pesticides increased from 20 to 65% in rice. Also, yield losses due to pests went up, with rice in Southeast Asia losing 30% of its output. The data make it clear how important it is to have integrated pest management (IPM) methods that can change as pests do.