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The present study was undertaken to assess the soil fertility status os soils under sugarcane-ratoon-wheat cropping system. Soil samples were collected from farmers fields at three depths viz., 0-15 cm, 15-30 cm and 30-45 cm and analysed for various soil properties. Results revealed that soils were sandy clay loam to clay loam in texture, neutral in soil reaction and non saline in salt content. Mean values of CEC 15.96, 13.13 and 10.80 C mol (p+) kg-1, SOC 4.57, 3.93 and 2.95 g kg-1, available nitrogen 263.01, 209.90 and 173.73 kg ha-1, available phosphorus 15.37, 12.49 and 9.00 kg ha-1, available potassium 156.43, 118.16 and 102.11 kg ha-1, DTPA Zn 0.49, 0.35 and 0.21 mg kg-1, DTPA Fe 9.53, 7.71 and 5.28 mg kg-1, DTPA Mn 8.20, 6.80 and 5.20 mg kg-1 and DTPA Cu 0.99, 0.74 and 0.56 mg kg-1 were recorded at 0-15 cm, 15-30 cm and 30-45 cm, respectively. Soils under this cropping system were low in available nitrogen content and medium in available phosphorus and potassium content. Among DTPA extractable micronutrients soil were deficient in available zinc while sufficient in available iron, manganese and copper content.

Agriculture is becoming more integrated in the agro-food chain and the global market, while environmental, food safety and quality are also increasingly impacting on the sector. It is facing with new challenges to meet growing demands for food, to be internationally competitive and to produce agricultural products of high quality. To cope with these challenges, Agriculture requires a continuous and sustainable increase in productivity and efficiency on all levels of agricultural production, while resources like water, energy, fertilizers etc. need to be used carefully and efficiently in order to protect and maintain the soil quality and environment. Consequently, Agriculture needs help in handling the complexity, uncertainty and fuzziness inherent in this domain. It requires new solutions for all aspects of agricultural farming, including precision farming and optimized resource application. Artificial Intelligence (AI) technology helps various industries to improve production and productivity. In agriculture, AI also allows farmers to increase their productivity and reduce negative environmental impacts. AI is changing the way our food is processed, where emissions from the agricultural sector have decreased by 20%. Together with precision agriculture (PA) and other emerging technologies, artificial intelligence (AI) can play a key role in modernizing agricultural practices and achieving the goal of improving the productivity of alternative arable cropping systems. In offering progressive change with advanced approaches, AI's future in agriculture is well ahead. The aim of this paper is to review various agricultural intelligence applications and to reduce the use of colossal amounts of chemicals with the aid of these technologies, resulting in reduced spending, improved soil fertility and increased productivity. With AI tools and machine learning, farmers can improve yields, protect their crops and have a much more reliable source of food.