{"id":1268,"date":"2020-03-18T14:51:01","date_gmt":"2020-03-18T14:51:01","guid":{"rendered":"https:\/\/www.thermofisher.com\/blog\/connectedlab\/?p=1268"},"modified":"2024-08-15T09:07:36","modified_gmt":"2024-08-15T09:07:36","slug":"using-ai-to-boost-crop-yields","status":"publish","type":"post","link":"https:\/\/www.thermofisher.com\/blog\/connectedlab\/using-ai-to-boost-crop-yields\/","title":{"rendered":"Using AI to Boost Crop Yields"},"content":{"rendered":"

As we discussed in the Machine Learning: A Primer to Laboratory Applications<\/a> blog, artificial intelligence (AI) and machine learning (ML) are helping researchers across many different industries uncover meaning in the massive amounts of data they collect and produce. The agriculture market is rapidly adopting these technologies. MarketsandMarkets<\/a> forecasts the AI in agriculture market will grow from $600 million in 2018 to $2.6 billion by 2025.<\/p>\n

<\/p>\n

What\u2019s driving this market growth? Research suggests the leading factor could be population growth. The global agriculture sector is facing major challenges from the lack of sufficient production with respect to the growing population. In the Artificial Intelligence: Applications and Global Markets report<\/a>, BCC Research projects that the agriculture sector needs to improve its production levels by over 70 percent to meet the demand of an expected global population of 9 billion by 2050. AI and ML are expected to play a key role in overcoming this challenge.<\/p>\n

AGROSAVIA Brings AI to the Field<\/h2>\n

One organization implementing AI to address these challenges is AGROSAVIA (Colombian Corporation for Agricultural Research). AGROSAVIA\u2019s mission is to contribute to technical change and improve productivity and competitiveness of the Colombian agricultural sector. As a public organization that operates independent of the Colombian government, AGROSAVIA uses research to drive scientific knowledge and development of agricultural technology in Colombia. Their network of laboratories is among the most comprehensive and modern infrastructures in the country.<\/p>\n

Working with the government of Colombia through the agency Ministry of Information Technologies and Communications (Ministerio de Tecnolog\u00edas de la Informaci\u00f3n y Comunicaciones), AGROSAVIA launched an initiative to optimize the soil analysis process. The project provides farmers with data-driven recommendations for crop fertilization before the planting season, with the goal of increasing crop output.<\/p>\n

To execute this ambitious project, AGROSAVIA implemented Thermo Scientific\u2122 SampleManager LIMS\u2122 software to manage their network of laboratories, centralize the soil data and make it accessible to the farming community. SampleManager LIMS software connects with IBM Watson\u2122 to utilize its AI capabilities. IBM Watson uses soil analysis data from SampleManager LIMS software and predicts which nutrients each crop needs. IBM Watson analyzes more than 10,000 fertilization plans and 10,000 soil studies when obtaining its model and algorithm. The fertilization plans detail the amount of nutrients that will be provided (e.g., Nitrogen, Phosphorus, Potassium, Calcium, Magnesium, Sulfur, Iron, Manganese, Zinc and Boron). The plans specify the amendment dose needed, fertilizer dose, and time to apply. The fertilization plans are also validated by an agronomist.\u00a0The agronomist\u2019s interaction with IBM Watson enables the tool to learn from revisions and improve its prediction.\u00a0<\/p>\n

Early Results help Farmers Embrace AI<\/h2>\n

By using SampleManager LIMS software with IBM Watson, AGROSAVIA has doubled the agronomists\u2019 ability to carry out fertilization plans. Before the project, agronomists completed 18 recommendations\/day. Using the new system, they\u2019re completing 36 recommendations\/day. AGROSAVIA has convinced farmers of the importance of the project, and provided the tools needed to help farmers participate.<\/p>\n

Farmers from around the country are sending their soil samples to AGROSAVIA, with the information captured and managed in SampleManager LIMS software. Automation from SampleManager LIMS software has made sample collection easier, improved the presentation of the fertilization plans, and increased sample traceability.<\/p>\n

AGROSAVIA expects even greater results as the program matures.\u00a0 The program currently includes fertilization plans for more than 200 crops.\u00a0 As additional plans are developed for each crop, the prediction models will continue to improve.<\/p>\n

This blog is the\u00a0second\u00a0in a series detailing how\u00a0Thermo Fisher software applications<\/a>\u00a0can support the use of advanced digital technologies, such as AI, to accelerate science. For more information about how AGROSAVIA is advancing agricultural technology in Colombia, read the SampleManager LIMS Agrosavia case study<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

As we discussed in the Machine Learning: A Primer to Laboratory Applications blog, artificial intelligence (AI) and machine learning (ML) are helping researchers across many different industries uncover meaning in the massive amounts of data they collect and produce. The agriculture market is rapidly adopting these technologies. MarketsandMarkets forecasts the AI in agriculture market will<\/p>\n","protected":false},"author":1029,"featured_media":1277,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_monsterinsights_skip_tracking":false,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[1],"tags":[591,589,112,95,499,102,96,590,226],"division":[],"class_list":{"0":"post-1268","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-uncategorized","8":"tag-artificial-intelligence","9":"tag-connected-lab","10":"tag-digital-transformation","11":"tag-lab-data-management","12":"tag-lab-of-the-future","13":"tag-laboratory-information-management-system","14":"tag-lims","15":"tag-machine-learning","16":"tag-samplemanager","17":"entry"},"_selected_authors":"","_selected_reviewers":"","acf":[],"yoast_head":"\nUsing AI to Boost Crop Yields<\/title>\n<meta name=\"description\" content=\"Research suggests the agriculture sector needs to boost production levels over 70% by 2050 to meet the growing population demands. 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