Early Diagnosis of Iron Deficiency in Commercial Tomato Crop Using Electrical Signals

Abstract

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Adequate plant nutrition is essential for commercial crop production. There are 18 nutrients that are essential for proper crop development. Each is equally important to the plant, although they are required in vastly different amounts. The absence of any one of these nutrients has the potential to decrease crop yields and quality by negatively affecting associated growth factors. Hence, early diagnosis of nutrient imbalances or deficiencies is of crucial importance for farmers. In this work, we provide compelling evidence that electrical potential variation in a commercial tomato crop contains information, which can be modeled to detect iron (Fe) deficiency before visual symptoms appear. The proposed supervised machine learning model showed accurate prediction on test data of above 75%. A model built to classify normal conditions (full nutrients) vs. strong Fe deficiency conditions (visible symptoms), enables early detection of slight Fe deprivation i.e., 6 days prior to the appearance of the earliest visual symptoms. Continuous real-time monitoring of crop electrical signals and deployment of predictive algorithms could constitute a great practical tool to help and assist farmers in iron deficiency detection.

Keywords

• electrical signal
• greenhouse
• iron deficiency
• machine learning
• classification