Artificial Intelligence: New Technology to Improve Indian Agriculture

Kumar, Rahul; Yadav, Shipra; Kumar, Mukesh; Kumar, Jitendra; Kumar, M Selva

doi:10.22271/chemi.2020.v8.i2at.9208

Cited by 11 publications

(14 citation statements)

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“…ese are the basic fundamental steps to explain this tree. e leaves represent the final outcomes, and the decision nodes represent the points at which the data is split [41,42]. Training data may be used for both regression tasks but is mostly employed for addressing classification issues.…”

Section: Decision Treesmentioning

confidence: 99%

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

Rakhra

Sanober

Naveed

et al. 2022

Journal of Food Quality

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Farmers’ physical labor and debt are reduced as a result of agricultural automation, which emphasizes efficient and effective use of various machines in farming operations with the purpose of reducing physical labor and debt. It is a revolutionary idea in agriculture to create custom hiring centers, which are intended to make it easier for like-minded farmers to embrace technology/machinery for enhanced resource management practices. The study in question examines the significance of tool renting and sharing in the workplace. Rental and sharing equipment are two approaches that might be used to enable farmers to borrow equipment at a cheaper cost than they would otherwise have to pay for it. The following is a manual pilot study of 562 farmers in India to address the numerous challenges farmers face when looking for tools and equipment, as well as to determine their strong interest in the process of renting and sharing equipment. The study was conducted to address the numerous challenges farmers face when looking for tools and equipment and to determine their strong interest in the process of renting and sharing equipment. Farmers are divided into three groups according to the results of this poll: small, moderate, and large. Training and testing splits were used on the same data set in order to get a better understanding of the target variables. The data set for the survey was standardized in order to remove ambiguity. In this research, three different machine learning models were utilized: nearest neighbors, logistic regression, and decision trees. K-nearest neighbors was the most often used model, followed by logistic regression and decision trees. In order to get the best possible result, a comparison of the aforementioned algorithm models was carried out, which revealed that the decision tree is the better model among the others in this regard. Because the decision tree model is completely reliant on a large number of input factors, such as the kind of crop, the time/month of harvest, and the type of equipment necessary for the crops, it has the potential to have a social and economic impact on farmers and their livelihoods.

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Section: Decision Treesmentioning

confidence: 99%

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

Rakhra

Sanober

Naveed

et al. 2022

Journal of Food Quality

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“…Fast and efficient identification of harmful microorganisms is critical for public safety biomonitoring in order to prevent foodborne illness and assure food security. Techniques for detecting microorganisms have progressed throughout the course of time [36,37]. Literature reported that survey centers on the AI applications according to four mainstays of food security that is food accessibility, food availability, food use, and strength [38].…”

Section: Introductionmentioning

confidence: 99%

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Rakhra

Bhargava

et al. 2022

Journal of Food Quality

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Several industries have recently seen the replacement of human labor by automated machinery and equipment. Across the globe, farmers’ attitudes on the use of technology in agriculture are divergent. However, although some people are excited and ready to embrace technology, others are cautious and wary of trying new technologies for the first time. The third category is particularly prevalent in underdeveloped nations such as India, owing to a lack of competence, a lack of effective translation, and most crucially, a lack of financial resources. It is fruitless for the government to attempt to resolve these difficulties due to the fact that they do not take into consideration the changing circumstances and input needs of each agricultural group. Smart Tillage is a cutting-edge framework that was developed to solve the challenges listed above. In India, a decision-based smart engine for the rental and sharing of tools and equipment has been developed, which leverages machine learning methods to proceed towards a selection of tools and equipment. The option is entirely reliant on a variety of input variables, including crop kind, harvest time/month, crop equipment needed, harvest type, and the amount of money available for rental. Additionally, an ideal recommendation engine driven by content and collaborative-based filtering will provide the farmer’s requirements depending on their requirements. In terms of escalation, the proposals would be cost-effective and excellent since they would need little changes in training, technique improvements, and resource management via a new rent-share model similar to that used by Uber. In this work, demand and supply algorithms are used to define market equilibrium, and the results are shown in graphs. This includes discussion of a variety of demand and supply parameters, their impact on market equilibrium prices and quantities, and their effect on shifting demand and supply curves. The many sorts of elasticities (demand, cross-price, supply, income, and so on) are examined, as well as the ramifications for pricing systems that may result from these elasticities.

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“…Among these, the Aspergillus genus stands out as a prolific producer of diverse metabolites with numerous biological activities. Some of its species also have potential applications in biotechnology and industry [4–8] . Notably, A. nidulans is recognized for its rich reservoir of bioactive metabolites, which exhibit a wide array of biological properties such as antioxidants, antibacterial, antiviral, plant growth promotion, cytotoxicity, anti‐inflammatory, and antimicrobial [8,9] .…”

Section: Introductionmentioning

confidence: 99%

“…Some of its species also have potential applications in biotechnology and industry [4–8] . Notably, A. nidulans is recognized for its rich reservoir of bioactive metabolites, which exhibit a wide array of biological properties such as antioxidants, antibacterial, antiviral, plant growth promotion, cytotoxicity, anti‐inflammatory, and antimicrobial [8,9] . In our program to discover antimicrobial agents from marine‐derived fungi, the ethyl acetate extract (EtOAc) of sponge derived‐fungus Aspergillus nidulans M256 exhibited antimicrobial activity (MICs: 128–256 μg/mL) against five‐microorganism strains including Gram‐positive bacteria ( Enterococcus faecalis , Staphylococcus aureus , Bacillus cereus ), Gram‐negative bacteria ( Salmonella enterica ), and yeast ( Candida albicans ).…”

Section: Introductionmentioning

confidence: 99%

Antimicrobial Activity of Depsidones and Macrocyclic Peptides Isolated from Marine Sponge‐Derived Fungus Aspergillus nidulans M256

Thi Hoang Anh,

Mai Anh,

Thi Thu Huyen

et al. 2023

Chemistry & Biodiversity

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Chemical study on marine sponge‐derivated fungus Aspergillus nidulans resulted in the isolation of seven depsidones (1–7) and two macrocyclic peptides (8 and 9). Their chemical structures were elucidated by extensive analyses of HRESIMS and NMR spectral data, as well as comparison with the literature. Compound 1 was an undescribed depsidone. All compounds exhibited significant antimicrobial activity (MICs: 2–128 µg/mL) towards at least one of seven microbial strains, including Bacillus cereus, Enterococcus faecalis, Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Salmonella enterica, and Candida albicans. Of these, chlorinated depsidones (1–3, and 5) displayed potential antimicrobial activity. Nidulin (2) possessed good activity against tested strains except for S. enterica with MIC values in range of 2–16 µg/mL. Interestingly, undescribed depsidone 1 was selectively bioactive on the Gram‐positive bacteria (MICs: 2‐4 µg/mL) and yeast (MIC: 8 µg/mL) but inactivity on the Gram‐negative bacteria (MICs: >256 µg/mL). Macrocyclic peptides, 8 and 9, displayed modest activity against E. faecalis strain with MIC values of 32 and 128 µg/mL, respectively.

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Artificial Intelligence: New Technology to Improve Indian Agriculture

Cited by 11 publications

References 0 publications

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Antimicrobial Activity of Depsidones and Macrocyclic Peptides Isolated from Marine Sponge‐Derived Fungus Aspergillus nidulans M256

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