Machine learning tools-based diagnosis of soil nutrient constraints to increase the productivity of citrus orchards

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Abstract
Introduction: The multiple nutritional disorders producing the early decline of citrus productivity are commonly observed across the citrus belts of northeast India. This situation is further compounded by a mismatch between annual addition and consumption of fertilizers, in the backdrop of an erroneous diagnosis of nutrient imbalance. In this background, we attempted to diagnose nutrient balance in Khasi mandarin ( Citrus reticulata Blanco) orchards using several diagnostic tools comprising machine learning (ML) tools. Methods: A database of soil available nutrients (KMn0 4 -N, Brays-P, NH 4 0Ac-K, DTPA-Fe, DTPA-Mn, DTPA-Cu, DTPA-Zn) and fruit yield documenting 180 Khasi mandarin orchards of seedling origin (10–30 years old with row-to-row distance of 4 m and trees 6 m apart) raised under rainfed conditions in the Meghalaya state of northeast India. Diagnosis methods were compared: the sufficiency level of available nutrients (SLAN), the basic cation saturation ratio (BCSR), the compositional nutrient diagnosis (CND), the diagnosis and recommendation integrated system (DRIS) and ML tools like random forest and xgboost. Results: Soil test interpretation of a low-yielding and nutritionally imbalanced orchard differed among diagnostic methods. DRIS predicted deficient-to-low concentrations of Zn, Ca, P, N, and K; other nutrients like Fe, Cu, Mn, and Mg were at optimum-to-high concentrations. CND standards diagnosed Zn deficiency and Cu excess with potential agronomic manifestations for early decline in productivity. SLAN interpretation was highly skewed for Ca; moderately skewed for Mg, Cu and Zn, and unskewed for N, P, K, Fe and Mn. The accuracy of ML regression models relating nutrient expressions to fruit yield was invariably high, followed by SLAN. The ML xgboost regression model exhibited the highest accuracy in predicting fruit yield from soil test. Conversely, the BCSR, which considers only three cationic dual ratios, was inaccurate. There were distortions when relating concentration values to DRIS indices to determine ‘optimum’ concentration ranges. The ML classification models showed that concentration values were also less accurate than clr to classify data as true negative (TN) or true positive (TP). The xgboost classification model showed optimum ranges for N, P, Ca, Mg, Mn, and Cu; whereas K, Fe, and Zn fell below the lower limits. Conclusion: ML, hence, as a classification approach, aided in discarding cases of poor yields and high yields showing luxury nutrient consumption or suboptimal nutrient levels. The soil test standards and site-by-site comparisons can further support site-specific nutrient management and precision fertilization. Considering Zn as the most deficient nutrient, Zn biofortification interventions are recommended to increase fruit yield and quality in Khasi mandarin.

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