Knowledge, Innovation and Technology in Scientific Research Vol. 4

Artificial Intelligence in Precision Agriculture for Quality Seed Production: A Critical Appraisal of Evidence, Methodological Limits and Translational Gaps

I. R. Delvadiya — 2026-10-13

Seed is the entry point of every crop production cycle, and the physical, physiological, genetic and health attributes of a seed lot constrain what any subsequent agronomic input can achieve. Precision agriculture has been extended into this domain through machine learning and deep learning applied to optical, spectral, radiographic, magnetic resonance and genomic data, with the stated purpose of replacing slow, destructive and partly subjective seed testing procedures and of tightening control over seed multiplication fields. This review evaluates that claim rather than restating it. Literature identified through Crossref, the Directory of Open Access Journals, Europe PMC, OpenAIRE, Semantic Scholar and Google Scholar, together with standards documentation, was appraised for methodological adequacy, evidence-claim alignment and translational readiness, and was synthesised around the measurement logic of each seed quality attribute rather than around algorithm families. The evidence is strongest and most internally consistent for tasks with a stable, externally auditable reference standard: varietal and species discrimination, detection of internal voids, insect damage and mechanical injury, and germination outcome after a completed germination test. Reported performance is markedly weaker, and reference standards markedly noisier, for seed vigour, for storage longevity and for field emergence, which are precisely the attributes that determine commercial value. Recurrent methodological weaknesses limit confidence across the corpus: reference standards are themselves imprecise, data partitioning frequently permits information leakage between images of the same seed lot, single-lot and single-season designs dominate, class imbalance is handled inconsistently, and models are rarely tested across cultivars, seasons, instruments or production regions. Economic and decision-analytic evaluation is almost entirely absent, and no study identified demonstrated equivalence with a recognised seed testing rule under independent audit. Evidence is geographically concentrated, with limited representation of the seed systems where quality deficits are most consequential. Priorities include multi-lot and multi-site validation, calibration-aware reporting, shared benchmark datasets with physiological ground truth, and explicit comparison against the incumbent test rather than against an untrained baseline.

Robotic Harvesting of Fruits and Vegetables: From Machine Vision to Intelligent Manipulation in Commercial Horticulture

Venkata Satish Kuchi — 2026-10-13

Robotic harvesting has progressed from proof-of-concept machines that separately addressed fruit detection, manipulator motion, or detachment to increasingly integrated systems that combine deep learning, three-dimensional sensing, compliant end-effectors, closed-loop control, and autonomous task planning. Yet commercial horticulture exposes a persistent gap between component-level performance and reliable harvest at crop scale. This critical narrative review evaluates that gap across fruit and vegetable harvesting systems, with emphasis on the coupled perception–action–crop problem rather than on isolated algorithms. Literature published from January 2000 through 18 July 2026 was examined across agricultural, engineering, robotics, and multidisciplinary scholarly sources, supplemented by citation searching and verification of bibliographic metadata. The evidence indicates that modern vision systems have substantially improved fruit recognition, instance segmentation, pose estimation, and picking-point localisation, but field performance remains constrained by occlusion, illumination change, depth uncertainty, fruit motion, clutter, and domain shift. Manipulation has similarly advanced from rigid grippers and simple cutters towards suction, soft and hybrid devices, yet compliance, hygiene, durability, detachment mechanics, fruit damage, and sensing remain tightly crop-dependent. Integrated field studies show that canopy architecture, crop preparation, target eligibility, retry logic, and the definition of success can influence reported performance as strongly as the selected perception or manipulation method. Consequently, cross-study comparisons based only on detection accuracy, harvest success, or per-fruit cycle time are often misleading. The strongest pathway towards commercial deployment is a system-level strategy that combines action-centric perception, uncertainty-aware manipulation, failure recovery, crop–robot co-design, and standardised field evaluation. Future progress should be judged by throughput, fruit quality, uptime, intervention frequency, maintainability, seasonal robustness, and economics, not solely by success on favourable targets.

From Forest Residue to Biorefinery: Cascade Valorization of Diospyros melanoxylon Foliar Biomass through Morpho-Physiological Dynamics, Green Solvent Extraction, Biocomposite Valorization, and Regional Techno-Economic Modeling

Aditya Raj Gupta — 2026-10-13

Diospyros melanoxylon Roxb. (Tendupatta) is an economically important non-timber forest product in central India, but physical grading generates a substantial stream of rejected foliage with limited value in the conventional bidi-centred chain. This study evaluates an interdisciplinary cascade-biorefinery framework that integrates foliar morpho-physiology, post-harvest stabilisation, aqueous-ethanol ultrasound-assisted extraction, lignocellulosic residue conversion, and regional techno-economic modelling. Coppice foliage showed a specific leaf weight of 81.5 ± 3.8 g/m², lamina thickness of 182.0 ± 11.0 µm, and apparent tensile elastic modulus of 14.8 ± 1.2 MPa, indicating distinct material characteristics relative to mature crown foliage. Elevated shade-rack curing reduced moisture to 11.2% within 72 h and lowered polyphenol oxidase activity by 64.2% relative to ground curing. Extraction with 70:30 v/v aqueous ethanol yielded 28.0% dry soluble extract and recovered diospyrin equivalents, betulinic acid, ursolic acid, oleanolic acid, and soluble tannins. The residual lignocellulosic marc was subsequently converted into starch-bound moulded packaging material. At the modelled regional scale, a 50 kg/h processing line operating for 3,000 h/year was linked to 20 decentralised aggregation hubs and 150 MT of annual sorting-discard feedstock. The baseline discounted-cash-flow model produced an NPV of ₹96.86 lakh at a 12% cost of capital. Overall, the framework demonstrates a potential route for diversified utilisation of rejected Tendupatta biomass, subject to analytical confirmation, independent replication, pilot-scale validation, and market qualification.

Accelerating Affordable Housing in India: AHP-Driven Decision Support for Choosing Optimal Fast-Track Construction Methods for Mass EWS Housing

Papolu Pramod — 2026-10-13

Securing adequate mass housing for the Economically Weaker Section (EWS) in India requires the selection of a structural system that optimally balances cost, construction speed, and long-term durability while addressing diverse site and implementation constraints. This study employs the Analytic Hierarchy Process (AHP) a robust multi-criteria decision-making (MCDM) technique chosen for its capacity to systematically quantify both tangible and intangible factors such as suitability, maintenance issues, and ease of working. Four advanced structural systems were evaluated against five criteria identified by a multidisciplinary expert panel comprising an academician, an architect, a structural engineer, a project manager, and a cost estimator. The AHP analysis revealed Cost Effectiveness (25.6%), Speed of Construction (22.9%), and Maintenance Issues (22.1%) as the three most influential criteria. Consistency ratios (CR ≤ 9.6%) confirmed the reliability of expert judgements. The final synthesis ranked the Monolithic Concrete System with Aluminium Formwork as the most suitable alternative, achieving the highest global priority weight of 33.9%, primarily due to its strong local priorities for Suitability for EWS Housing (0.558) and Low Maintenance Requirements (0.477). The Factory-Made Steel Frame System followed closely at 28.8%, excelling in criteria related to construction speed and logistics. In contrast, the conventional RCC Framed System was consistently rated lowest (16.5%) and deemed unsuitable for large-scale, time-bound housing programmes. The study concludes that industrialised and non-traditional structural systems particularly monolithic concrete technologies offer a superior balance of cost efficiency, rapid deployment, and durability, making them critical to achieving India's ambitious EWS housing objectives.

Physical and Mechanical Characterization of BPT 2766 Paddy Seeds for Precision Planter Design

G. Sindhuja — 2026-10-13

The present study was conducted in 2025 at Dr. NTR College of Agricultural Engineering, Bapatla, Andhra Pradesh, to characterise BPT 2766 paddy seeds and obtain engineering parameters for precision planter development. The physical and mechanical properties of seeds are important for the design of precision seed-metering and handling systems. Seed dimensions, geometric properties, density, porosity, thousand-seed weight, angle of repose, coefficient of static friction and moisture content were determined using standard procedures. The average seed length, width and thickness were 7.77, 1.96 and 1.70 mm, respectively, with a geometric mean diameter of 2.95 mm. Sphericity and aspect ratio were 37.73 and 25.23%, while bulk density, true density and porosity were 493.52 kg m⁻³, 1053.93 kg m⁻³ and 52.88%, respectively. The thousand-seed weight and angle of repose were 14.32 g and 27.23°, respectively. Static friction varied from 0.36 to 0.53 across the tested surfaces, and moisture content was 10.40% (dry basis). The results provide essential engineering data for the design of seed-metering, hopper and seed-flow components for BPT 2766 paddy.

Generative Artificial Intelligence and the Future of Human Expertise: A Narrative Review of Cognitive Offloading, Deskilling, Learning, and Capability Renewal

Kwan Hong TAN — 2026-10-13

Aims: This chapter reviews how generative artificial intelligence (GenAI) is changing the relationship between immediate task performance and the long-term development of human expertise across workplace and educational settings. It integrates evidence on cognitive offloading, automation, learning science, professional skill formation and human-AI collaboration to examine when AI assistance augments capability and when it risks weakening it.

Study Design: Traditional narrative review.

Methodology: The review synthesises peer-reviewed literature and formally accepted articles using a traditional narrative approach. Targeted scholarly searches, searches of publisher and journal platforms, and citation chaining were used to integrate foundational studies of external cognition, automation and expertise with recent field experiments and observational studies of GenAI in work and education. The analysis is organised around mechanisms rather than a formal systematic-review protocol.

Results: Evidence consistently shows substantial short-run productivity benefits from GenAI, often strongest for less experienced users. For example, a three-company randomised study involving 4,867 software developers reported a 26.08% increase in completed tasks with AI assistance, while a high-school mathematics experiment found that unguarded GPT-4 access could improve practice performance yet reduce subsequent unaided performance. Assisted performance and independent competence are therefore not equivalent outcomes. Risks arise when AI replaces effortful retrieval, self-explanation, error diagnosis, independent judgement and participation in apprenticeship pathways. Conversely, carefully scaffolded tutoring, active collaboration, partial automation, deliberate practice and AI-generated simulations can strengthen learning.

Conclusion: The central policy and design problem is not whether cognition should be offloaded, but which cognitive activities may be safely delegated, at what stage of learning, and with what mechanisms for preserving calibration, transfer and recovery. Organisations and educators should evaluate AI systems against dual objectives: present performance and future unaided capability.