Plant diseases cause significant damage to agriculture, leading to substantial yield losses and posing a major threat to food security. Detection, identification, quantification, and diagnosis of plant diseases are crucial parts of precision agriculture and crop protection. Modernizing agriculture and improving production efficiency are significantly affected by using computer vision technology for crop disease diagnosis. This technology is notable for its non-destructive nature, speed, real-time responsiveness, and precision. Deep learning (DL), a recent breakthrough in computer vision, has become a focal point in agricultural plant protection that can minimize the biases of manually selecting disease spot features. This study reviews the techniques and tools used for automatic disease identification, state-of-the-art DL models, and recent trends in DL-based image analysis. The techniques, performance, benefits, drawbacks, underlying frameworks, and reference datasets of more than 278 research articles were analyzed and subsequently highlighted in accordance with the architecture of computer vision and deep learning models. Key findings include the effectiveness of imaging techniques and sensors like RGB, multispectral, and hyperspectral cameras for early disease detection. Researchers also evaluated various DL architectures, such as convolutional neural networks, vision transformers, generative adversarial networks, vision language models, and foundation models. Moreover, the study connects academic research with practical agricultural applications, providing guidance on the suitability of these models for production environments. This comprehensive review offers valuable insights into the current state and future directions of deep learning in plant disease detection, making it a significant resource for researchers, academicians, and practitioners in precision agriculture.

Global warming and climate change are warnings showcasing water crisis. At the same time ever growing population is ultimatum to the food security. In span of such times, world has to be made a sustainable habitat. It is only possible when each ounce of resources is being measured and used judiciously. Maximum responsibility is on farmers and researchers of the world. In times of advanced technologies, Internet of Things (IoT) has surfaced as a saviour. IoT based systems have been stated as success in monitoring and control mechanisms. Thus, this paper was intended to review the control strategies and monitoring systems based on IoT. The literature incorporates basic information as well as recent trends in the field of irrigation management based on IoT.

Plastic mulches are widely used for cultivation of vegetables. An experiment was conducted to observe the impact of plastic colour mulches on soil temperature, water use efficiency (WUE) and produce of tomato (Solanum lycopersicum) under drip irrigation from 2015-2016. The treatments were laid out in split plot design with three replications. The plastic coloured mulches used were white on black, silver on black and black. 60, 80, 100 and 120% evapotranspiration (ET) used as irrigation levels and a non-mulched treatment as control. Due to the interaction effects, treatment white colour on black plastic mulch with 80% ET i.e., I₂M₁ recorded the maximum yield (93.54 t ha^-1) followed by silver colour on black plastic mulch with 80% ET (I₂M₂) produced 87.65 t ha^-1. Plastic colour mulching improved production compared to bare soil. as the plants grown in white colour on black plastic colour mulch resulted 40 to 54% increase in yield compared to control treatment.