Irrigating farms the smarter way—a study on the utilization of precision irrigation by vegetable farmers from South 24-Parganas district, West Bengal, India

Precision irrigation is a novel concept that optimizes water use precisely when and where needed, thereby enhancing crop productivity and water use efficiency. It involves accurate monitoring of crop and soil parameters to determine the appropriate amount of water for healthy plant growth and crop production. The present study highlights the usage of precision irrigation across vegetable fields practicing monoculture farming in the Baruipur, Sonarpur, and Jaynagar blocks of South 24-Parganas district, West Bengal, India, from April 2024 to March 2025. Cultivation of Trichosanthes dioica, Abelmoschus esculentus, Cucurbita maxima, Cucumis sativus, Luffa acutangula, and Trichosanthes cucumerina was practiced. This study involves an array of sensors (soil, moisture, temperature, humidity, crop growth monitoring, and soil nutrient analyzer), algorithms, and drip tip and sprinkler irrigation for formulating an optimum irrigating schedule. K-nearest neighbors, logistic regression, support vector machine, decision tree, random forest, and the gradient boosting algorithm were used for analysis using the Raspberry Pi microprocessor. A watering schedule was designed based on the signal generated by a microcontroller. Sprinkler irrigation at 50% Depletion of Available Soil moisture (DASM) was employed for the studied cropping system. Thus, in a typical crop production system, water productivity (WP) was defined as the relationship between crops produced and the amount of water provided for the said purpose. WP in the crop field under the traditional watering schedule recorded values between 18.22 kg/ha/cm^. (Cucurbita maxima) and 48.65 kg/ha/cm (Trichosanthes dioica). However, utilizing precision irrigation techniques yielded results varying between 22.15kg/ha/cm (Trichosanthes cucumerina) and 52.68 kg/ha/cm (Cucumis sativus). The Random Boost algorithm (accuracy=98.20% for Cucurbita maxima), random forest (accuracy=97.5 % for Trichosanthes cucumerina), and decision tree (accuracy=97.20% for Trichosanthes cucumerina) were found to provide the most accurate results. In spite of the prevalence of small, illiterate landholders, adoption of such digitized technologies appears quite rewarding for the farming community.