The importance of fresh and wholesome food for the growing global population cannot be overstated, and greenhouses and indoor agricultural techniques play a crucial role in ensuring this. Over the past two decades, hyperspectral imaging research has made significant advancements, and its potential in horticulture is expected to continue growing in the coming years. However, there are still challenges to overcome for its widespread applicability.
“As the world embraces Industry 4.0, the horticulture industry will be at the forefront of innovation, with cutting-edge technologies that enable us to feed a growing population while reducing our impact on the planet.”
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One area where hyperspectral imaging and AI algorithms have shown promising results is in the automated detection of pests and diseases in plants, which enables effective monitoring of large-scale fruit and vegetable crops. Timely detection of pests and diseases enhances pest and disease management systems, and previous studies have shown that AI algorithms can be implemented in horticulture for various applications, such as fruit detection, pest and disease detection, weed detection, plant stress detection, and yield prediction using spectroscopy and camera systems.
For example, a study was conducted to identify common pests and diseases in apple fruit using sparse coding and computer vision techniques. These techniques can identify pest and disease-damaged fruits and provide data to assist in the early detection and treatment of diseases and pests. Additionally, soil-organic-carbon (SOC) monitoring is crucial for assessing soil quality and fertility, and the use of artificial neural networks (ANN), cubist regression, support-vector machines (SVM), multiple linear regression (MLR), and random forests (RF) have been applied to soil-nutrient indicators, total catchment area, and topographic-wetness index data to improve SOC prediction.
Furthermore, the application of AI in the drying process of fruits and vegetables, combined with efficient physical fields, has gained attention for generating better-dried fruit and vegetable products. IoT-based tools, using machine-learning algorithms, can determine whether a climacteric fruit has been artificially ripened or not, which has implications for food quality and safety. Additionally, combining computer vision techniques with autonomous robotic systems that utilize deep-learning concepts of artificial intelligence can enable intelligent spraying, crop yield prediction, price forecasting, predictive insights, and disease diagnosis.
Industry 4.0 technologies, such as hyperspectral imaging, AI algorithms, IoT, and sensors, in horticulture has shown promising results in automated pest and disease detection, soil monitoring, fruit ripening assessment, and food waste reduction. The integration of computer vision techniques with autonomous robotic systems and the use of machine learning and IoT can further enhance the efficiency, productivity, and sustainability of horticultural practices in India and beyond.
Udayan Arya
