Lindsay Bets on AI to Help Farmers Improve Profitability as Irrigation Sales Decline

Lindsay Corporation is leaning more heavily on artificial intelligence (AI) and digital agriculture tools as weak farm economics continue to weigh on irrigation equipment sales, with executives positioning software as a long-term growth driver during the company's fiscal third-quarter earnings call.

The irrigation equipment maker reported fiscal third-quarter revenue of $160.8 million, down 5% year over year, while diluted earnings per share came in at $1.53, ahead of analyst expectations. The company said North American irrigation demand remains subdued as growers continue delaying capital purchases amid challenging commodity prices, high production costs, and ongoing trade uncertainty. 

Despite the softer equipment market, Lindsay expects technology revenue to grow at a double-digit rate during fiscal 2026, reflecting a broader shift among agricultural equipment manufacturers toward software and connected services even as machinery demand slows.

AI Takes a Larger Role in Irrigation Management

Management devoted a significant portion of the earnings call to the company's AI initiatives, particularly FieldNET Advisor, which uses AI models to recommend irrigation schedules based on weather conditions, crop growth stages, and soil characteristics.

"With FieldNET Advisor specifically, we've really been deploying a lot of AI models to help with irrigation scheduling," President and CEO Randy Wood said during the earnings call. "We're putting all of that intelligence in the pocket of our customers, and they can wake up every morning and know exactly what water is required where, based on historical weather, predicted weather, crop growth stage, soil type." 

FieldNET Advisor is part of Lindsay's connected irrigation platform, which enables growers to remotely monitor and manage irrigation systems while optimizing water use and operational efficiency.

The company also highlighted SmartPivot, where machine learning is being used to identify potential equipment failures before they occur.

"If you have machine learning capabilities to recognize what normal looks like, you can calculate what abnormal looks like and again, pre-diagnose some of those failures," Wood said. 

The focus on predictive maintenance shows a trend across industrial sectors, where AI is increasingly being used to reduce equipment downtime and improve operational efficiency.

AI Investment Continues Despite Weak Farm Economics

Lindsay's emphasis on AI comes as its core irrigation business continues to face pressure from cautious customer spending.

Irrigation segment revenue fell 7% year over year to $133 million, with North American irrigation revenue declining 11% as farmers postponed equipment purchases. International irrigation revenue also slipped 4%, primarily because of weaker sales in Brazil. 

Wood said many growers remain under financial strain, making technologies that improve efficiency more valuable than ever.

"When you look at customers right now selling commodities for less than it costs them to grow them, we've got to find ways to enhance our profitability wherever we can," he said. 

According to the U.S. Department of Agriculture (USDA), elevated production expenses and softer farm income continue to pressure agricultural profitability, contributing to delayed investment in new equipment.

Rather than scaling back technology investments during the downturn, Lindsay said it continues to invest in digital solutions while restructuring parts of its business to improve efficiency. Wood said the restructuring initiative includes "leveraging tools like AI to change how we work" while maintaining investment in innovation and manufacturing capabilities. 

The company expects restructuring savings to begin in fiscal 2027 while continuing to expand its recurring technology business. Management also said adoption of its FieldNET and FieldWise platforms continues to accelerate, strengthening customer retention and supporting a higher-margin revenue mix.

Lindsay's strategy mirrors a broad shift across agriculture, where AI is increasingly being applied to practical operational challenges such as irrigation management, resource optimization, and predictive maintenance instead of standalone automation projects.