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Shrimp farming is a multibillion-dollar industry, with production expected to reach 6 million metric tons in 2023. The industry also faces numerous challenges, such as a rise in the cost of aquaculture input (feed, energy, transportation), volatile demand that impacts the farmgate price, diseases and unstable climatic conditions that affect survival and growth rates. An additional challenge, especially in Asia, is the fragmented status of the farming industry, with numerous stakeholders involved in the postlarvae production, the supply of animal health products and feed, and the farming and harvesting before delivery to the fresh market or the processors.
In order to improve production efficiency, farmers first looked at ways to optimise feeding, i.e., their highest cost. It started with automated feeders to deliver feed over longer periods of time and reduce feed wastage while controlling labour costs. However, feeding must be adapted to the shrimp’s weight, health status, and rearing conditions. This requires farmers to regularly check feed intake (using a feeding tray), measure water parameters (temperature, dissolved oxygen, pH, ammonia, nitrite, minerals, etc.), and assess the shrimp size and health status.
This is where new technologies started to play key roles:
• Measurement of physicochemical parameters using IoT probes allows farmers to have an accurate and real-time overview of the rearing condition in their ponds, implementing changes and feeding management. Issues with the probes’ cost and the need for self-cleaning and calibration (shrimp pond condition favour fouling) delayed their rollout.
• Measurement of shrimp size or weight using image analysis. Although it still requires a representative sample to be collected, it facilitates the calculation of the shrimp growth rate. Based on this calculation, water parameters and historical data, shrimp health is estimated and the feeding protocols and pond management protocols are revised.
• Feed management can also be adapted based on the real-time measurement of feed intake. Without information on the shrimp’s health or size, the feeding regime would be revised based on the monitored feeding needs. The feeding protocol can be controlled either by farmers using the report for the decision or an algorithm.
In order to improve production efficiency, farmers first looked at ways to optimize feeding, i.e. their largest cost
• A successful crop depends upon the stocking of clean and strong post-larvae. AI tools can support farmers by quickly reporting their size (average and, importantly, range and variability) and health indicators. Further developments are expected in this area leading to a quick assessment of the stocked post-larvae.
• Optimisation of the feeding protocol (including the functional feed to improve the immune system) depends upon a correct diagnosis of the cultivated animals. This can be costly and time-consuming. Development in this area will allow frequent assessment of the shrimp’s health and the consequent optimisation of feeding and pond management.
• With the correct assessment of the shrimp growth rate and size, health and rearing conditions, harvests are optimised considering the carrying capacity of the pond and market demand. Several tools that combine traditional sonar technology with artificial intelligence, machine learning and computer vision provide accurate estimates of standing biomass, further optimising carrying capacities and planning harvest.
• The next step is the development of platforms that connects the farmers with the suppliers (post-larvae, feed, animal health products), financing services and processors.
The shrimp industry is witnessing fast changes with the arrival of new companies bringing fresh approaches to measurement, analysis and recommendations. The current challenging market conditions may speed up the adoption of new technologies with clear and immediate benefits. Other technologies may require additional time for their fine-tuning and adoption.
