Smart farming is a development that has emphasized information and communication technology used in machinery, equipment, and sensors in network-based hi-tech farm supervision cycles. Innovative technologies, the Internet of Things (IoT), and cloud computing are anticipated to inspire growth and initiate the use of robots and artificial intelligence in farming. Such ground-breaking deviations are unsettling current agriculture approaches, while also presenting a range of challenges. This paper investigates the tools and equipment used in applications of wireless sensors in IoT agriculture, and the anticipated challenges faced when merging technology with conventional farming activities. Furthermore, this technical knowledge is helpful to growers during crop periods from sowing to harvest; and applications in both packing and transport are also investigated.
Crop yield data is critical for managing sustainable agriculture and assessing national food security. Current study aims to increase Peanut productivity from current levels by analyzing the yield gap of production potential between theoretical yield and actual farmers’ yields. The spatial yield gap of Peanut for Thiruvannamalai district of Tamil Nadu is examined in this paper by integrating the products of microwave remote sensing (SAR Sentinel-1A) with DSSAT CROPGRO peanut simulation model. CROPGRO Peanut model was calibrated and validated by conducting field experiment at Oilseeds Research Station, Tindivanam during Rabi 2019 for predominant cultivars viz. TMV 7, TMV 13, VRI 2 and G 7. Actual attainable yield was recorded by organizing CCE with help of Department of Agriculture Economics and Statistics in the respective monitoring Villages. Regression analysis between maximum recorded DSSAT Leaf Area Index (LAI) at peak flowering stage of peanut and yield recorded by Crop Cutting Experiment (CCE) for spatial yield estimation of Peanut in Thiruvannamalai district of Tamil Nadu during Rabi 2021 was carried out using ArcGIS 10.6 software. The results showed that the simulated potential yield ranged from 3194 to 4843 kg/ha, whereas actual yield ranged from 1228 to 3106 kg/ha, with a considerable disparity between the actual and potential yield levels (1217 to 2346 kg/ha) of the monitored locations. The minimum, maximum and average yield gaps in Peanut for Thiruvannamalai district was assessed as 1890, 2324 and 2134 kg/ha, respectively. To reduce the production difference (Yield gap) of Peanut cultivation, farmers should focus more on management issues such as time of sowing, irrigation or water management, quantity and sources of nutrients, cultivar selection and availability of quality seeds tailored to each region.
The increased land-use change (LUC) from native lands to other land use at the Conoor region of western ghats in Tamil Nadu has severely declined soil carbon concentration. Therefore to quantify this decline, Carbon Management Index (CMI) was worked out under major land uses {(Forest (FOR), cropland (CRP), tea plantation (TEA)} using total organic carbon (TOC) and carbon pools under varying degrees of lability {a) NLC (non-labile carbon) b) VLC (very labile carbon) c) LC (labile carbon) d) LLC (less labile carbon)}. Results portray that the carbon pools were significantly (p < 0.05) higher in FOR than in TEA and CRP. The contribution of active pools {(very labile carbon (VLC) and labile carbon (LC)} towards TOC was higher in TEA and CRP, whereas in FOR, the passive pool {(less labile carbon (LLC) and non-labile carbon (NLC)} was higher. TOC (0-45 cm) was concentrated on the surface soils of FOR (32.88 g kg-1), CRP (11.87 g kg-1) and TEA (18.84 g kg-1) and it gradually declined with the increase in depth. The decline in TOC was maximum between 0 – 15 and 15 – 30 cm depth in CRP (30.62%) and FOR (22.17%), whereas it was maximum (37.16%) between 15 -30 and 30 -45 cm depth in TEA. Therefore, LUC spotlights the degradation of carbon pools and its extent was quantified using the carbon management index (CMI). The CMI (0 – 45 cm) recorded at CRP (12.93) and TEA (32.62) signals the need for an implementation of carbon management strategies at Conoor to keep the soils alive and protect biodiversity.
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