Electronics in Agriculture


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Dr. R. T. Patil Central Institute of Agricultural Engineering, Bhopal Opportunities for Electronics in Agriculture in India

Production & Post Harvest Scenario:

Production & Post Harvest Scenario Agriculture contributes about 17.5% of GDP, employees 57% workforce and sustains approx over 70% of the population India produces about 230 million tons of food grains and 53.1 and 91.6 million tons of fruits and vegetables and ranks second in world however losses are 3-18% Low level of processing of fruits and vegetables at only 2% . Food processing is employment intensive, creates 1.8 jobs directly and 6.4 indirectly for every Rs. 10 lakh investment

Researchable Issues:

Researchable Issues Online non destructive measurement of quality of food grains and fruits and vegetables using NIR sensors Electronic nose to assess the quality and authenticity of food products. Electronic tongue - for recognition (identification, classification, discrimination), quantitative multi-component analysis and artificial assessment of taste and flavour of various liquids Affordable instrumentation for measurement of spoilage of grain in bags and silos Smart labels of food packets to detect their shelf life with automatically changing bar codes Simple gadgets like pH meter to detect pollutants in drinking water

Laser Assisted Land Leveling:

Laser Assisted Land Leveling The laser-controlled system requires a laser transmitter, a laser receiver, an electrical control panel and a twin solenoid hydraulic control valve. The laser transmitter transmits a laser beam, which is intercepted by the laser receiver mounted on the levelling bucket. The control panel mounted on the tractor interprets the signal from the receiver and opens or closes amount of soil that must be cut.

Detection of Plant Diseases:

Detection of Plant Diseases Electronic nose incorporating artificial intelligence was used to detect plant disease, specifically basal stem rot (BSR) disease that is caused by Ganoderma boninense fungus affecting oil palm plantations in South East Asia. The commercially available electronic nose, Cyranose 320, as the front end sensors and artificial neural networks for pattern recognition. The odour samples were captured on site and the classification performed on a PC. The system was able to differentiate healthy and infected oil palm with a high rate of accuracy.

Automatic Fruit and Vegetable Classification from Images:

Automatic Fruit and Vegetable Classification from Images Face recognition, fingerprinting identification, image categorization, and DNA sequencing is high tech application The fusion approach was validated using a multi-class fruit-and-vegetable categorization task in a semi-controlled environment, such as a distribution center The results show that the solution is able to reduce the classification error in up to 15 percentage points with respect to the baseline.

ANN in image recognition and classification of crop and weeds:

ANN in image recognition and classification of crop and weeds The images were taken . Colour index values were assigned to the pixels of the indexed image and used as ANN inputs. There were 80 images, 100x100 pixels, for training, and 20 images for testing. Many back propagation ANN models were developed with different numbers of PEs in their hidden and various output layers. Six different evaluation schemes for two ANN output strategies were used. The performance of the ANNs was compared and the success rate for the identification of corn was observed to be as high as 80 to 100%, while the success rate for weed classification was as high as 60 to 80%. The results indicated the potential of ANNs for fast image recognition and classification. Fast image recognition and classification can be useful in the control of real-world, site-specific herbicide application.

Identification of citrus disease using color texture and discriminant analysis:

Identification of citrus disease using color texture and discriminant analysis Color co-occurrence method (CCM) with texture based hue, saturation, and intensity (HSI) color features in conjunction with statistical classification algorithms were used to identify diseased and normal citrus leaves under laboratory conditions. The leaf sample discriminant analysis using CCM textural features achieved classification accuracies of over 95% for all classes. Although, high accuracies were achieved when using an unreduced dataset consisting of all HSI texture features, the overall best performer was determined to be a reduced data model that relied on hue and saturation features. This model was selected due to reduced computational load and the elimination of intensity features, which are not robust in the presence of ambient light variation.


Conclusions World is faced with an ever-growing demand for food from limited resources on which to grow it, but with the help of developments in electronics and other engineering disciplines we make possible the further intensification of agriculture. For this we need to save labor, increase precision, reduce food loss and the time it takes to plant and harvest through engineering interventions

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