ARTIFICIAL INTELLIGENCE BASED SUSTAINABLE COTTON CROP PRODUCTION USING REMOTE SENSING DATASET

Abstract

The decline in cotton production in Pakistan has directly impacted the country's foreign exchange earnings, plummeting from twelve million bales in 1991, the number decreased to four million bales in 2020. Considering a substantial portion of Pakistan's exports are linked to the textiles area, with 40% of jobs tied to production, this decline is concerning. Factors such as climate change and changes in biotic and abiotic stresses have contributed to substantial losses in cotton production. In Pakistan, where 90% of farmers face resource constraints, there is a pressing need for more accurate mechanisms to understand the challenges in crop production. Timely acknowledgment and adoption of more flexible approaches to irrigation, fertilizer application, and pest and disease control are crucial. Excessive use of pesticides has exacerbated the situation, leading to the development of resistance among pests and diseases. Traditional visual inspection methods alone are insufficient for predicting crop health effectively, resulting in deprived crop yields and financial losses for agrarians. Enhancing crop health condition requires leveraging information gathered from remote sensing and terrestrial sensors, along with truth field surveys. By processing this data through tools like the Decision Support System for Agrotechnology Transfer (DSSAT) and employing machine learning techniques such as artificial neural networks, more accurate local and field-level advice can be provided to farmers. This approach aims to optimize crop management practices, reduce losses, and ultimately improve farmers' incomes.