Autonomous agricultural equipment are rapidly evolving across the globe. Digitalization, automation, and artificial intelligence are playing a major role in crop production, including weeding, harvesting, pest control, among others. Autonomous equipment in the agriculture sector support environmentally sustainable practices. Such equipment allow spot weeding and precision management of pests, nutrients, weeds, and diseases, through mechanical removal or spot application of chemicals. Autonomous equipment also helps to substitute labor shortage, particularly when there is limited availability, therefore increases social sustainability.
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Agricultural automation offers power and equipment requirements for organizing the soil and maintaining, establishing, storing, processing agricultural crops on the farm. For the last 50 years, the agriculture sector has been witnessing evolution, from basic hand tools, animal-powered implements to sophisticated engine-powered equipment. However, in some developing countries, conventional agriculture equipment are still common, which is hampering agricultural productivity and negatively affecting the livelihoods of small-scale farmers. Digitalization in the agriculture sector helps in the development of the farm by reducing drudgery and eliminating hard work during labor peaks.
Furthermore, some of the leading companies operating in the global autonomous agriculture equipment market are AGCO Corporation, CLAAS KGaA mbH, Deere & Company, Kubota Corporation, Mahindra & Mahindra, CNH INDUSTRIAL N.V., YTO Group Corporation, ISEKI & CO., LTD., and Yanmar Co. Ltd.
The agriculture sector has been witnessing various technological advancements that have led to the development of various types of agriculture equipment. Various autonomous agricultural equipment advancements use machine vision technology to prevent hazards, determine whether crops are ready to be harvested or not and identify crops. Such equipment involves multiple cameras feeding data to the autonomous equipment that allows it to locate and access the crops around it. It also performs tasks such as harvesting, weed picking, sorting, growth monitoring, and packing.
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Autonomous agriculture equipment habitually depends on GPS information to position and locate themselves on the field. Another technology utilized in autonomous agricultural equipment is machine learning. Machine learning offers an advanced method of identifying collision paths; it helps autonomous vehicles to adapt and avoid new or unexpected hazards in their paths.
The global autonomous agriculture equipment market research study offers a broad perspective on the analysis of the industry. The research is based on extensive primary interviews (in-house experts, industry leaders, and market players) and secondary research (a host of paid and unpaid databases), along with the analytical tools that have been used to build the forecast and the predictive models.
