Innovations in automation with robots and sensor technologies for postharvest sorting of perishable food

The significant postharvest loss of perishable foods, mainly due to inefficiencies in handling, sorting, and quality assessment, underscores the critical need for advanced automation solutions in the food supply chain.

This paper explores developing and implementing robotic systems for postharvest handling and sorting perishable foods, focusing on innovations in

• sensor technology, 
• machine learning, and
• soft robotics 

to improve quality retention and reduce damage. 

It provides an overview of the challenges faced in postharvest logistics and examines the role of automated systems in enhancing efficiency and scalability within the agricultural sector.

Integrating quality assessment sensors, such as hyperspectral imaging, and machine-learning algorithms facilitates real-time produce sorting based on key quality indicators.

At the same time, soft robotics offer a solution for gently handling fragile items.

Through case studies and performance evaluations, this study illustrates how robotic systems can effectively address labor shortages, minimize food waste, and improve supply chain transparency, ultimately contributing to sustainable food systems and increased profitability for stakeholders across the value chain.

Introduction

Perishable products are prone to spoilage and loss of freshness postharvest. Approximately 1.3 billion metric tons of food produced for human consumption are wasted annually, leading to an estimated US$1 trillion in spoils (Durán-Sandoval et al., 2023).

Globally, food supply routes are increasingly being threatened due to the entry of pollutants, contaminants, and toxic elements. Modern consumers are grossly inconvenienced by purchasing perishables whose intrinsic and extrinsic qualities are masked or hidden and, therefore, find it difficult to correctly assess the actual decay state (Makanjuola et al., 2020).

Speedy and efficient sorting and segregation of food commodities are apparent needs for all stakeholders involved from training, appraising, research, and extension perspectives (Kaur et al., 2023; Daszkiewicz, 2022; Khan et al., 2024; Adedeji, 2022).

Automation in the postharvest handling of fruits and vegetables is the most effective solution for minimizing or eliminating food waste for the retailer or the consumer (Krishnamma et al., 2024).

Interest in handling food through technological intervention shows a paradigm shift in the commercial philosophy of operation.

In addition to working with rural produce such as fruits and vegetables, it provides an opportunity to transfer value to individual farmers who aspire to have a more significant role in the food production chain.

Modern and advanced robotic-based systems, with their better speed and efficiency, are beneficial to fulfilling the demand for quick sorting of fruits and vegetables without causing any damage to the products (Zhou et al., 2022) Chauhan et al., 2022).

The robotic systems for postharvest handling have more advantages than traditional methods, which can grade the products based on several physical, mechanical, and electrical properties. The computer vision and machine learning-based sorting systems also provide high-quality product grading at a lower cost. These systems have a crucial effect on lowering postharvest losses significantly and greatly help decision-making (Singh et al., 2022; Vrochidou et al., 2022).

Contents

• OVERVIEW OF POSTHARVEST CHALLENGES WITH PERISHABLE FOODS
• SIGNIFICANCE OF AUTOMATION IN REDUCING POSTHARVEST LOSSES
• ROBOTIC SYSTEM DESIGN AND SPECIFICATION
• Integration of sensors for quality assessment (ripeness, color, size)
• Development of Soft Robotics for Gentle Handling
• Sensor Technology for Automated Quality Sorting
• Machine learning algorithms for automated sorting
• DISCUSSION
• Comparison with traditional handling methods

Conclusion

The advancements in robotic systems for the postharvest handling and sorting of perishable foods represent a paradigm shift in agricultural logistics and food quality management.

Robotic systems with advanced sensor technology and machine learning algorithms provide a highly efficient and scalable alternative to traditional, labour-intensive handling methods.

Deploying qualityassessment sensors and applying soft robotics for gentle handling is crucial in minimising damage, reducing waste, and maintaining fresh produce's nutritional and aesthetic quality.

Despite the high initial investment, the long-term benefits, including lower operational costs, extended shelf life, and enhanced marketability of produce, make robotic systems a promising solution for the agricultural industry.

However, widespread adoption requires overcoming scalability, cost, and workforce retraining barriers. Collaboration among technology providers, industry stakeholders, and regulatory bodies is essential to standardise best practices and facilitate industry-wide integration.

Future research should focus on refining sensor accuracy, exploring adaptive soft robotics for varied produce, and developing cost-effective models to broaden accessibility.

As these technologies continue to evolve, robotic systems hold the potential to revolutionise postharvest operations, supporting a more resilient and sustainable food supply chain.

Tables

The paper also contains several interesnting tables:

• Table 1: Case Studies on Sensor Technologies for Quality Assessment in Perishable Food Sorting
• Table 2: Overview of Soft Robotic Grippers for Gentle Handling of Fresh Produce
• Table 3: Comparison of Sensor Technologies for Automated Quality Sorting in Perishable Foods
• Table 4: Machine learning algorithms applied in automated sorting of perishable foods (Ngongoma, 2024; Miraei Ashtiani et al.,
  2021)
• Table 5: Comparison between Traditional Handling Methods and Automated Systems in Postharvest Sorting

Source

Jadhav V., Sancheti, S., Sanghavi, K., Sanghavi, M., & Sancheti, D. 2024. 
Advanced Robotic Automation and Sensor Technologies for Postharvest Sorting of Perishable Foods: Innovations, Case Studies, and Future Perspectives
Journal of Postharvest Technology 2024, 12 (4): 1-12
http://jpht.in/MenuscriptFile/69366c13-b065-4610-8cee-d3870e5b6b27.pdf
https://doi.org/10.48165/jpht.2024.12.4.01

Figure is Figure 1 of the original paper, Representation of the roles of robotics in the post-harvest supply chain.