Remote Sensing and Machine Learning for Futuristic Horticulture: A Comprehensive Review

Authors: Jadala Shankaraswamy; Md Abdul Gaffor
Remote Sensing and Machine Learning for Futuristic Horticulture: A Comprehensive Review
DOI
10.25125/ijoear-aug-2026-26
DIN
IJOEAR-AUG-2026-26
Abstract

Horticultural crops—fruits, vegetables, plantation crops, flowers, spices and medicinal plants—form the nutritional and economic backbone of India's agricultural sector. However, they face persistent challenges of yield variability, water and nutrient inefficiency, pest and disease pressure and post-harvest losses. This review synthesises the transformative role of Remote Sensing (RS) and Machine Learning (ML) in enabling futuristic, sustainable and precision horticulture. The evolution of agricultural production systems from Agriculture 1.0 to Agriculture 5.0 is traced, followed by a comprehensive discussion of RS principles, elements, four fundamental resolutions (spatial, spectral, temporal, radiometric), and multi-platform architectures (satellite, aerial, UAV, ground-based). A parallel taxonomy of ML—supervised, semi-supervised, unsupervised, reinforcement and deep-learning frameworks—is presented, with emphasis on Random Forest, Support Vector Machine, XGBoost, and Convolutional Neural Network algorithms most widely applied to horticultural datasets. Five representative case studies are analysed in depth: (i) UAV multispectral + ensemble learning for leaf nitrogen monitoring in custard apple [1]; (ii) hyperspectral + ANN / RF / DT models for mango and strawberry fruit-quality parameters [2]; (iii) UAV-hyperspectral detection of citrus canker [3]; (iv) machine-learning-based crop recommendation under NPK, pH and climatic variables [4]; and (v) RS + GIS soil-site suitability for six major fruit crops in the Ganjigatti sub-watershed, Karnataka [5]. National platforms such as CHAMAN, Bhoonidhi, Bhuvan and PMFBY are reviewed as operational demonstrations of the RS + ML value chain. Finally, technical, economic and ethical challenges—ground-truth scarcity, sensor cost, model interpretability, digital divide and data privacy—are discussed, and a way forward for Indian horticulture in the Agriculture 5.0 era is proposed.

Keywords
Remote sensing machine learning precision horticulture UAV hyperspectral imaging vegetation indices XGBoost convolutional neural network Agriculture 5.0 decision support system.
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Introduction

Horticulture constitutes a vital component of the world's agricultural systems, contributing significantly to food and nutritional security, rural livelihoods, export earnings and agro-industrial development. Fruits, vegetables, spices, plantation crops and ornamental plants deliver essential vitamins, minerals, antioxidants and dietary fibre indispensable to human health and well-being. In developing economies such as India, horticulture has emerged as a high-growth sector by virtue of higher productivity per unit area, greater income potential and expanding domestic and international markets [6].

Despite its importance, horticultural production faces numerous challenges, including declining soil fertility, water scarcity, climate variability, pest and disease outbreaks, labour shortages and rising input costs. Traditional horticultural practices, largely dependent on farmer experience and visual field observations, often result in inefficient input use, yield variability and environmental degradation. These limitations have accelerated the need for advanced technologies that can support precise, timely and site-specific decision-making [7], [8].

Remote Sensing (RS) and Machine Learning (ML) together offer transformative solutions by enabling non-destructive, rapid and large-scale monitoring of crops and orchards. RS technologies provide continuous observations of crop condition across spatial and temporal scales, while ML algorithms extract meaningful patterns and predictive insights from complex, high-dimensional datasets. Their integration supports informed decision-making related to irrigation scheduling, nutrient management, disease control, harvest planning and yield forecasting [1], [2], [7], [9].

While several reviews have addressed RS and ML in agriculture broadly, this review specifically focuses on horticultural crops—a sector with unique characteristics (perennial nature, high-value produce, complex canopy structures) that pose distinct challenges and opportunities for RS + ML applications. Furthermore, this review integrates the Indian national context, covering operational platforms such as CHAMAN, Bhoonidhi, Bhuvan and PMFBY that are rarely discussed together in the international literature. The review also explicitly connects the evolution from Agriculture 1.0 to Agriculture 5.0 with the technological readiness of RS + ML systems, providing a forward-looking perspective on intelligent horticulture.

This review synthesises the state of the art of RS and ML for horticulture as of 2026. Section 2 traces the evolution of agricultural systems from Agriculture 1.0 to Agriculture 5.0. Sections 3 and 4 present the technical foundations of RS and ML respectively. Section 5 discusses the synergistic integration of RS and ML, and Section 6 presents five case studies. Section 7 reviews Indian national operational platforms (CHAMAN, Bhoonidhi, Bhuvan, PMFBY). Section 8 discusses limitations, and Section 9 concludes with a way-forward road-map.

Conclusion

Remote Sensing and Machine Learning constitute two mutually reinforcing pillars of futuristic horticulture. RS provides multi-scale, multi-temporal, multi-spectral windows into the orchard environment, while ML converts the resulting high-dimensional data into actionable knowledge. Together, they are reshaping horticulture across every operational domain—yield forecasting, disease detection, nutrient monitoring, irrigation, fruit-quality assessment, weed detection, site-suitability planning and crop insurance. Case-study evidence from India, China, USA and Egypt demonstrates R² values of 0.66–0.94, accuracy > 90% and RMSE reductions of one order of magnitude against classical statistical methods.

For India, the confluence of Agriculture 5.0 principles, open-data platforms like Bhoonidhi and Bhuvan, national missions like MIDH-CHAMAN and PMFBY, and a growing agri-tech start-up ecosystem creates an unprecedented opportunity to leap-frog into intelligent, sustainable horticultural production. Addressing the current limitations of ground-truth scarcity, sensor cost, model interpretability and digital divide through coordinated policy, technology transfer and capacity building will determine how quickly this promise is realised. The next decade of Indian horticulture will be defined not merely by hectares under cultivation, but by the depth of digital intelligence guiding every hectare.

Limitations of the Review: This review is based on a narrative synthesis of published literature rather than a formal systematic review. The case studies were selected to represent diversity in crops, sensors and ML approaches, but may not be exhaustive of all relevant research. The review focuses primarily on Indian and international studies published in English, potentially overlooking research in other languages or regional contexts.

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