Title: Field evaluation of a deep learning-based smart variable-rate sprayer for targeted application of agrochemicals
Journal: Smart Agricultural Technology
Abstract: The field performance of a newly developed novel smart variable-rate sprayer was evaluated. The sprayer uses convolutional neural networks (CNNs) for target detection and spot-applications of agrochemicals within potato (Solanum tuberosum L.) fields attacked by lamb’s quarters (Chenopodium album L.) and corn spurry (Spergula arvensis L.) weeds and the early blight potato disease caused by Alternaria solani Sorauer. There was a non-significant effect of treatment conditions (i.e., cloudy, partly cloudy, and sunny) on spray volume during weed and diseased plant detection experiments (p-value = 0.93 and 0.75, respectively) showing that the smart sprayer performed well during all treatment conditions. There was a significant effect of spraying application techniques on the use of spray volume (p-value ≤ 0.05) reflecting a significant saving of spraying liquid during variable-rate application (VA). On average, the sprayer reduced spray volume by 47 and 51% for weeds and diseased plant detection experiments as compared to the values of chemicals applied at constant-rate application (CA), respectively, under all treatment conditions. The analysis of water-sensitive papers (WSP) data resulted in non-significant differences between CA and VA under all field conditions. These results suggest that this sprayer has a great potential to get a suitable spot application of agrochemicals and reduce the use of plant protection products thereby ensuring farm profits and environmental stewardship.
Title: Mitigation of Greenhouse Gas Emissions from Agricultural Fields through Bioresource Management
Abstract: Efficient bioresource management can alter soil biochemistry and soil physical properties, leading to reduced greenhouse gas (GHG) emissions from agricultural fields. The objective of this study was to evaluate the role of organic amendments including biodigestate (BD), biochar (BC), and their combinations with inorganic fertilizer (IF) in increasing carbon sequestration potential and mitigation of GHG emissions from potato (Solanum tuberosum) fields. Six soil amendments including BD, BC, IF, and their combinations BDIF and BCIF, and control (C) were replicated four times under a completely randomized block design during the 2021 growing season of potatoes in Prince Edward Island, Canada. An LI-COR gas analyzer was used to monitor emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from treatment plots. Analysis of variance (ANOVA) results depicted higher soil moisture-holding capacities in plots at relatively lower elevations and comparatively lesser volumetric moisture content in plots at higher elevations. Soil moisture was also impacted by soil temperature and rainfall events. There was a significant effect of events of data collection, i.e., the length of the growing season (p-value ≤ 0.05) on soil surface temperature, leading to increased GHG emissions during the summer months. ANOVA results also revealed that BD, BC, and BCIF significantly (p-value ≤ 0.05) sequestered more soil organic carbon than other treatments. The six experimental treatments and twelve data collection events had significant effects (p-value ≤ 0.05) on the emission of CO2. However, the BD plots had the least emissions of CO2 followed by BC plots, and the emissions increased with an increase in atmospheric/soil temperature. Results concluded that organic fertilizers and their combinations with inorganic fertilizers help to reduce the emissions from the agricultural soils and enhance environmental sustainability.