The stakes are enormous. Rice is the primary food source for more than 3.5 billion people, and global production must rise by an estimated 25 to 30 percent by 2050 to feed a projected population of 9.7 billion. Rice blast, caused by the fungus Magnaporthe oryzae, routinely destroys 10 to 30 percent of harvests and can wipe out entire crops under epidemic conditions. The pathogen attacks every above-ground part of the plant, using specialized infection structures called appressoria that generate enormous turgor pressure to physically rupture the leaf cuticle. Conventional defenses, resistant varieties, fungicides such as strobilurins, triazoles, and tricyclazole, and agronomic management, are steadily being undermined: the fungus’s high genetic variability rapidly overcomes resistance genes, and fungicide overuse fuels both resistance evolution and food-safety concerns.

The new review, led by Sandip Garai of the Indian Council of Agricultural Research’s Institute of Bioresources and Sustainable Agriculture with colleagues including Suryakant Manik, Kanaka K.K., Sudhir Kumar, Vijai Pal Bhadana, and Nishit Malviya, followed the PRISMA 2020 reporting standard. The team searched PubMed, Scopus, Google Scholar, and Semantic Scholar in May 2026, retrieving 2,056 records. After a two-pass deduplication combining exact DOI matching with fuzzy title matching, followed by automated keyword screening and full-text assessment, 109 studies survived. Screening reliability was checked on a random 30-record subsample by a second author, yielding a Cohen’s kappa of 0.86, indicating almost perfect agreement.

Artificial intelligence dominates the field. Of the 109 studies, 86 involved AI or machine learning, and 62 used convolutional neural networks, the deep learning architectures that revolutionized computer vision. Transfer learning from ImageNet-pretrained weights is nearly universal, with ResNet, VGG, MobileNet, InceptionNet, EfficientNet, and the YOLO family of object detectors all represented. The single highest reported accuracy in the corpus is 99.75 percent, achieved by ResNet50 classifying blast-infected versus healthy leaves on a 2,000-image dataset. A hybrid architecture called ResViT-Rice, which couples a ResNet backbone with a Vision Transformer encoder and attention modules, reached 99.04 percent accuracy while providing built-in explainability through attention maps. Object-detection studies, which localize lesions as well as classify them, span the full YOLO lineage from version 5 through 11 and include specialized variants such as Pyramid-YOLOv8, which targets small early-stage lesions, and YOLO-RBSD, which detects M. oryzae spores in microscopic images at 96.1 percent mean average precision and 125 frames per second.

Yet the review’s performance analysis reveals a sobering pattern. When 64 verified accuracy values were grouped across six detection tasks, median accuracy for laboratory-validated image classification studies has remained essentially flat, around 96 percent, since 2021, despite a flood of new architectures. Field-deployed studies report a median of 94 percent, a modest gap in absolute terms but one that masks the real problem: only nine studies across the entire corpus were validated in open fields. The authors conclude that the bottleneck is no longer model capacity but evaluation context, lighting variability, complex backgrounds, pathogen diversity, and growth-stage effects that laboratory datasets fail to capture. Notably, the correlation between dataset size and reported accuracy was only weak to moderate (r = 0.43, not statistically significant), suggesting that dataset curation, class balance, and evaluation rigor may matter more than sheer volume of images.

Beyond imaging, remote sensing is emerging as the most agronomically consequential frontier. Nineteen studies used ground-based spectroradiometers, hyperspectral cameras, UAV sensors, Raman and near-infrared spectroscopy, and satellites including Sentinel-2. The landmark capability is pre-symptomatic detection: identifying infection before any visible lesion appears. One team used machine learning-based feature selection to identify diagnostic wavelengths in the red-edge region between 700 and 740 nanometers, detecting blast at the asymptomatic stage with 92.9 to 94.8 percent accuracy. Another group developed a dedicated rice blast vegetation index, validated in UAV field trials with 95.9 percent overall accuracy, substantially outperforming the standard NDVI. Chlorophyll fluorescence imaging, which detects photosynthetic disruption rather than reflectance changes, offers similar pre-symptomatic power at roughly a tenth of the hardware cost of hyperspectral systems, a critical consideration for resource-poor farmers.

Drones and the Internet of Things round out the field-scale picture. Eleven UAV studies covered both detection and precision spraying; one showed that adding 1 percent lecithin adjuvant to drone-applied fungicide cut off-target drift by up to 3.16 times while increasing deposition along the spray path by up to 155 percent. On the sensing side, twelve IoT studies included platforms such as RiceBioS, which runs machine learning models on edge devices in low-connectivity fields and reduced latency by 40 percent compared with cloud processing, and AgriTalk, a cloud-fog-edge architecture that halved latency for farmers. Perhaps the most conceptually novel contribution is an electronic nose: a metal-oxide sensor array that detects volatile organic compounds characteristic of M. oryzae infection days before any visual or spectral signal appears, the only approach in the entire corpus capable of continuous, non-imaging, pre-visual monitoring.

Against these advances, the review documents three glaring absences. A systematic audit confirmed that the terms blockchain, digital twin, and federated learning appeared in none of the 2,056 records retrieved, not merely none of the 109 included studies. Blockchain has matured in agricultural supply-chain traceability elsewhere but has never been applied to rice blast surveillance or disease reporting; federated learning, which would allow institutions across blast-endemic regions to train shared diagnostic models without surrendering data ownership, is similarly unexplored; and digital twins, virtual crop-system replicas fusing sensors, weather data, and epidemiological models for scenario planning, are entirely absent. The authors project research horizons of 2025 through 2028 for closing these gaps, noting that the underlying technologies are already mature in other domains.

The review also scrutinized the quality of the literature itself. Using a purpose-built scoring framework called AI-Agri-QS, which rates dataset adequacy, validation rigor, metric completeness, and novelty on a 0-to-8 scale, the team found a mean score of just 1.50, with 89 percent of studies classified as low quality. Four systematic weaknesses recur: homogeneous single-source datasets shot under controlled lighting; roughly 40 percent of studies reporting only top-1 accuracy while omitting precision, recall, and F1-score; geographic concentration in East and South Asia with near-total neglect of sub-Saharan Africa and Latin America; and a complete absence of multi-season validation despite the year-to-year variability of blast epidemics. Only seven studies, 6 percent, incorporated explainable AI methods such as Grad-CAM or SHAP, a notable shortfall given that agronomists need to understand why a model flags a field.

The bottom line, the authors argue, is a field that is technically advanced but deployment-limited. The research agenda they propose centers on multi-site, multi-season field trials; affordable pre-symptomatic detection for smallholders; blockchain-enabled disease traceability; federated learning for privacy-preserving distributed training; explainable AI for trust and regulatory acceptance; and digital twin frameworks for epidemic simulation. For the farmers who bear the heaviest blast burden, many of them managing less than a hectare without reliable connectivity, the difference between a 99 percent laboratory score and a tool that works in a muddy paddy at dawn remains the decisive measure. The next generation of rice blast technology, the review concludes, will be judged not on benchmarks but on fields.

The research, recently published in the journal Biochar, presents an experimental investigation into the effects of biochar-enhanced potting media on Ocimum basilicum, commonly known as basil. Utilizing smart growth cabinets equipped with high-resolution cameras and a battery of environmental sensors, the study monitored basil plants under controlled conditions over a 30-day growth period. This setup allowed real-time tracking of crucial growth parameters such as leaf area expansion, root development, ambient humidity, and light intensity, providing a granular understanding of plant responses to various substrates.

Central to the study were six distinct growth media formulations, meticulously designed to juxtapose traditional soil-based mediums against advanced soilless counterparts incorporating sand, coconut coir, and perlite. Among these, biochar—a highly porous carbonaceous material derived from the pyrolysis of organic waste—was evaluated both in untreated form and enriched with nutrients to ascertain its dual role as a soil conditioner and slow-release fertilizer. The physical and chemical properties of biochar, such as high cation exchange capacity and superior water retention, underpinned hypotheses about its potential to enhance nutrient availability and root aeration for potted herbs.

The empirical results were compelling. Substituting 10 to 20 percent of conventional potting mix with nutrient-enriched biochar not only bolstered root mass and leaf development but also resulted in an approximate threefold increase in biomass accumulation compared to media containing untreated biochar. This underscores the significance of biochar’s nutrient profile and its capacity to serve as a matrix for controlled nutrient release, thereby reducing the dependency on synthetic fertilizers that often contribute to environmental degradation and greenhouse gas emissions.

Intriguingly, the study found that biochar’s benefits are highly contingent on both its application rate and treatment status. Excessive biochar incorporation or the use of untreated biochar blends with sand and coir exhibited inhibitory effects on basil growth, emphasizing the necessity for optimizing biochar formulations tailored to specific crop requirements. These findings highlight a precision agriculture perspective, where biochar application rates and compositions are fine-tuned to maximize plant productivity while mitigating potential growth stressors.

The deployment of IoT-driven smart growth cabinets played an instrumental role in elucidating these nuanced responses. The continuous monitoring of microenvironmental variables enabled a detailed temporal correlation between plant physiological status and substrate characteristics. Such real-time data acquisition promises to advance predictive models of plant growth dynamics and nutrient uptake, fostering an era where digital agriculture can finesse material inputs for sustainable food production with unmatched accuracy.

Beyond the immediate agronomic improvements, the implications of integrating biochar into potting mixes extend to climate change mitigation and the circular economy. Biochar’s ability to sequester stable carbon compounds for decades or even centuries in soil matrices positions it as a potent tool for carbon dioxide drawdown. Furthermore, its production valorizes agricultural and forestry residues, transforming biomass waste streams into valuable horticultural amendments, thus closing the loop in organic waste management and promoting resource efficiency.

The researchers advocate for further longitudinal studies to investigate biochar’s long-term nutrient release patterns and interaction with microbial communities in soilless systems. Understanding these dynamics is crucial for scaling biochar applications to commercial horticulture, potentially replacing conventionally applied substrates like perlite, which have notable environmental footprints due to mining and non-renewable extraction methods.

Moreover, the team envisions that the amalgamation of biochar amendment with smart sensing technologies could serve as a blueprint for sustainable intensive agriculture beyond basil, adaptable to various herbs, vegetables, and ornamental plants. Such integration aligns with global efforts to develop resilient food systems in the face of soil degradation, water scarcity, and climate unpredictability, underscoring the transformative potential of combining traditional soil science with modern digital innovation.

Lead author Sirjana Adhikari emphasizes the dual advantage of this approach: “Biochar-enhanced growth media not only drive superior plant performance but also contribute significantly to carbon sequestration strategies. The synergy between biochar’s physical properties and IoT-enabled monitoring offers a revolutionary pathway to climate-friendly, productive horticulture.”

This convergence of environmental sustainability, technological innovation, and practical agriculture heralds a promising frontier. As smart agriculture technology becomes more accessible and biochar production methodologies are refined, farmers, urban gardeners, and agricultural industries worldwide may soon adopt biochar-enriched soilless substrates as standard practice. Such advancements hold the promise of elevating crop yield and quality while preserving ecological balance within a rapidly changing climate paradigm.

Ultimately, this study casts biochar not merely as a growth enhancer but as a multifaceted agent of change—enhancing plant nutrition, fostering sustainable waste management, and supporting climate mitigation efforts. Through data-rich, sensor-driven cultivation experiments, the research sets a precedent for future explorations into how innovative materials science and IoT solutions can collectively drive the next green revolution in horticulture.

Subject of Research: Not applicable

Article Title: Optimizing sustainable basil cultivation with smart-monitoring: a comparative study of biochar and soilless growth media

News Publication Date: 3-Jul-2025

Web References:
Biochar Journal
DOI Link

References:
Adhikari, S., Vernon, M., Adams, S., Webb, L., & Timms, W. (2025). Optimizing sustainable basil cultivation with smart-monitoring: a comparative study of biochar and soilless growth media. Biochar, 7:89.

Image Credits: Sirjana Adhikari, Michael Vernon, Scott Adams, Lawerence Webb & Wendy Timms

Keywords: Horticulture, Sustainable agriculture