The Journal of Agricultural Technology and Innovation (JATI) publishes cutting-edge research in the fields of agricultural science, technology, and sustainable innovation. The current issue includes articles on the latest advancements in agricultural practices, precision farming, biotechnology, and sustainable crop management.
Featured Articles in the Latest Issue
- Volume 3(Issue 1) JANUARY- JUNE 2026
Research Articles
Autonomous UAV-Based Chlorophyll Mapping for Precision Nitrogen Management in Commercial Wheat Farms
Vol.3(1); Pages:1-14. Published on April 2026
Abstract
Precision nitrogen management remains one of the most critical challenges in modern cereal production due to increasing fertilizer costs and environmental concerns associated with nutrient losses. This study evaluated the effectiveness of unmanned aerial vehicle (UAV)-based multispectral imaging for chlorophyll mapping and variable-rate nitrogen application in large-scale wheat farms. Experimental plots were established across three commercial farms with varying soil textures and climatic conditions. UAV imagery was collected at four growth stages using multispectral sensors capable of detecting near-infrared and red-edge reflectance. Vegetation indices including NDVI and chlorophyll estimation ratios were integrated into a geospatial decision-support platform for generating variable-rate fertilizer prescriptions. Results demonstrated that UAV-assisted nitrogen management improved nitrogen use efficiency by 18.6% while reducing fertilizer application by approximately 14% compared with conventional uniform treatments. Grain protein content and yield stability were also enhanced in variable-rate zones. Economic assessment revealed a positive return on investment through reduced input costs and improved productivity. The findings confirm that drone-assisted chlorophyll mapping provides a scalable and environmentally sustainable strategy for precision nutrient management in commercial wheat production systems. The study further emphasizes the role of digital agriculture technologies in improving farm-level decision-making under variable climatic and soil conditions.
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Biodegradable Nano-Coated Seed Pellets for Enhancing Germination Efficiency in Salinity-Stressed Tomato Cultivation
Vol.3(1); Pages:15-27. Published on April 2026
Abstract
Salinity stress significantly restricts seed germination and early seedling establishment in vegetable crops, particularly in arid and semi-arid agricultural systems. This research investigated the development of biodegradable nano-coated seed pellets designed to enhance germination performance of tomato seeds under saline conditions. Seed pellets were formulated using biodegradable polymer matrices enriched with zinc oxide nanoparticles and humic acid compounds. Germination tests were conducted under controlled salinity levels ranging from 50 to 150 mM sodium chloride. Physiological indicators including germination percentage, root elongation, seed vigor index, and antioxidant enzyme activity were evaluated over a 21-day growth period. Results indicated that nano-coated seed pellets increased germination rates by 23% under moderate salinity and improved root biomass accumulation compared with untreated controls. Enhanced catalase and superoxide dismutase activities suggested improved oxidative stress tolerance in treated seedlings. Additionally, the biodegradable coating maintained moisture retention around the seed microenvironment, contributing to improved emergence consistency. Greenhouse validation further demonstrated improved transplant survival and early vegetative growth. The study concludes that nano-enabled biodegradable seed coatings represent a promising technological innovation for sustainable vegetable production in salt-affected agricultural regions. Adoption of such seed technologies may support climate-resilient crop establishment and improve productivity under increasingly challenging soil conditions.
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Machine Learning Prediction Models for Early Detection of Maize Foliar Diseases Using Hyperspectral Imaging
Vol.3(1); Pages:28-40. Published on May 2026
Abstract
Early detection of foliar diseases is essential for minimizing yield losses and optimizing pesticide application in maize cultivation. This study developed machine learning-based predictive models utilizing hyperspectral imaging data for rapid identification of major maize foliar diseases including northern corn leaf blight and gray leaf spot. Hyperspectral images were collected from infected and healthy maize plants under controlled and field environments using wavelengths ranging from 400 to 1000 nm. Spectral signatures were analyzed using principal component analysis and integrated into supervised machine learning algorithms including random forest, support vector machine, and convolutional neural networks. Model accuracy, sensitivity, and computational efficiency were compared to determine the most effective classification approach. Results demonstrated that convolutional neural networks achieved the highest classification accuracy of 95.4%, outperforming conventional algorithms in disease differentiation and early-stage symptom detection. Disease symptoms were identified up to seven days before visible field manifestation, enabling earlier management interventions. The predictive system also reduced false-positive pesticide recommendations through adaptive learning optimization. Findings suggest that integrating hyperspectral sensing with artificial intelligence can significantly improve disease surveillance in precision agriculture systems. The proposed framework offers substantial potential for automated crop health monitoring and reduced chemical dependency in sustainable maize production.
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Solar-Powered IoT Irrigation Controllers for Water Conservation in Greenhouse Lettuce Production
Vol.3(1); Pages:41-54. Published on May 2026
Abstract
Increasing pressure on freshwater resources has accelerated the development of intelligent irrigation technologies capable of improving water use efficiency in controlled agricultural environments. This study evaluated the performance of solar-powered Internet of Things (IoT) irrigation controllers in greenhouse lettuce cultivation. The system incorporated soil moisture sensors, solar-powered communication nodes, automated valve controls, and cloud-based monitoring software for real-time irrigation scheduling. Experimental trials were conducted over two crop cycles comparing IoT-managed irrigation with conventional timer-based systems. Parameters including water consumption, plant biomass, leaf area development, and energy efficiency were monitored continuously. Results revealed that the IoT-controlled system reduced irrigation water usage by 31% while maintaining equivalent or higher lettuce yields relative to traditional irrigation practices. Improved root-zone moisture regulation also reduced nutrient leaching and minimized physiological stress during high-temperature periods. Solar energy integration provided stable autonomous operation with minimal maintenance requirements and reduced external energy dependency. Economic analysis indicated favorable cost recovery potential for medium-scale greenhouse enterprises through lower water and energy expenditures. The study demonstrates that combining renewable energy systems with sensor driven irrigation management can significantly enhance greenhouse sustainability. The proposed approach offers practical opportunities for climate-smart horticultural production and efficient resource utilization in protected cultivation systems.
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Comparative Evaluation of Biochar-Enriched Compost on Soil Carbon Sequestration and Strawberry Yield Performance
Vol.3(1); Pages:55-67. Published on May 2026
Abstract
Soil degradation and declining organic carbon levels present major constraints to long-term agricultural sustainability. This study assessed the effects of biochar-enriched compost amendments on soil carbon sequestration and strawberry crop productivity under intensive cultivation conditions. Field experiments were conducted using four amendment treatments including conventional compost, biochar-enriched compost, mineral fertilizer, and untreated control plots. Soil physicochemical properties, microbial biomass, carbon retention, fruit yield, and quality parameters were evaluated over two growing seasons. Results indicated that biochar-enriched compost significantly improved soil organic carbon accumulation and enhanced microbial activity relative to conventional organic amendments. Strawberry plants grown in treated plots exhibited higher fruit yield, improved berry firmness, and increased soluble sugar content. Soil moisture retention and cation exchange capacity also increased substantially in biochar-amended soils, contributing to improved nutrient availability and reduced irrigation demand. Carbon sequestration analysis revealed greater long-term stabilization of organic carbon fractions in biochar treatments compared with traditional compost applications. The integration of biochar with compost materials therefore provides both agronomic and environmental benefits for sustainable horticultural production. The findings support wider adoption of carbon-focused soil amendment strategies aimed at improving productivity while contributing to climate change mitigation and resilient agricultural ecosystems.
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